以一個愛國（只看中文）的奇幻小說迷觀點來講
這篇論文...
糟透了...........
如果他想要寫一篇國際性論文
那他不應該將亞洲奇幻小說加入研究中
應該單純只從世界奇幻名著裡論述、發展
他既然有將中文亦或是亞洲小說列入評比
那就應該花心思去理解、跟取樣
看他的論文結果可以發現
他取樣的可能大多是高知識份子
因為結論都是洋文著作比較有名
狗屁！！！！！！
中國文學4、5000年，語文發展的底蘊是其他東西拍馬所能及的嗎？
就算你搭蒸汽機、時光機我都敢保證跟不上！
光中國用來損人的字眼就可以出好幾套書了！！
而且這些取樣對象（高知識份子）真的可以稱得上是迷嗎？
他取樣對象和那些每天下班就鑽入租書店的上班族、工人和學生（被標籤為不認真）相比稱得上是迷嗎？
另外，既然他敢用奇幻兩個字
敢問真的理解奇幻是什麼嗎？
我自認每天閱讀非傳統武俠風格的小說不下10000字
我都不敢拍胸向任何人保證哪些是奇幻名著
因為說真的，奇幻可以無所不包，也可以不包所有
此外起點隨便找一個名作家，可能收入與知名度都遠遠超過他問卷所列的很多作者、作品
話說回來
國外的奇幻風格翻來覆去也是那幾種點子
狼人、吸血鬼、魔法師、希臘眾神（近來多個異能與魔獸）
因為他們只有中古歐洲的傳說
其實光是埃及的神系
這複雜度就遠遠不是西方世界能比得上的
更不用講中國人超愛造神
一匹紅馬跟一個紅臉漢
就可以讓人回味無窮，更不用說是其所在的時空環境了
一段封神、一場尋秦、一部山海...
這些都可以是奇幻，也都不是奇幻
這種可以出書的研究題目只寫了不到200頁
x大的素質越來越令人憂心
雖然我遠遠比不上他們，但是重點是現在的素質真的大不如前...
2010年1月28日 星期四
2009年3月2日 星期一
2009年2月15日 星期日
Improving End User Behaviour in Password Utilization: An Action Research Initiative
這篇論文主 要是探討如何幫助一般用戶創建安全程度夠高的密碼並且易於個人記憶，這樣的密碼至少需要15個組成，其中包含2個數字與1個符號，但是根據古典認知研 究：humans can memorize only seven plus or minus two chunks of information (Miller 1956)
1.action research is ‘‘ideally suited to the study of technology in its human context’’ (Baskerville and Wood-Harper 1996, p. 235).
（1）為了去記憶強健的密碼有必要記憶某些技術和策略，因此讓使用者可以不必寫下來，不會忘記它們，或不必在多個帳戶使用相同的密碼
2008年12月22日 星期一
Smart Signs Showing the way in Smart Surroundings
Smart Signs Showing the way in Smart Surroundings
View SlideShare presentation or Upload your own.
2008年11月24日 星期一
2008年11月17日 星期一
Indoor-Outdoor Positioning and Lifelog Experiment with Mobile Phones
Indoor Outdoor
View SlideShare presentation or Upload your own.
2008年11月3日 星期一
Improving RFID Read Rate Reliability by a Systematic Error Detection Approach
improvement RFID
View SlideShare presentation or Upload your own.
2008年9月29日 星期一
2008年7月28日 星期一
2008年7月18日 星期五
2008年6月20日 星期五
Statistical Analysis of the Social Network and Discussion Threads in Slashdot
We analyze the social network emerging from the user comment activity on the website Slashdot.
Using Kolmogorov-Smirnov statistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distri- butions.
We also study the structure of discussion threads using an intuitive radial tree representation.
Threads show strong heterogeneity and self-similarity throughout the dif- ferent nesting levels of a conversation.
We use these results to propose a simple measure to evaluate the degree of con- troversy provoked by a post.
To improve the quality and the representativity of the resulting graph, we filter some of the comments according to the following four criteria:
1.The post: Under this assumption, no relations exist between the post’s author and its direct commentators, unless he also participates later in the discussion.
2.Anonymous comments were also discarded.
3.We discard very low quality comments with score −1.
4.Finally, we filter out self-replies, often motivated by a forgotten aspect or error fix of the original comment.
The size of the big cluster for strongly connected components is of course, smaller.
gesting that the small-world property is present in all of them.
The quantities are approximately one unit lower than the corresponding value for a random graph ℓrand.
The maximal distance D between two users is also very small.
Even for the undirected sparse case, it only takes a maximum of eleven steps to reach a user starting randomly from any other.
These results are also in accordance with similar studies of other traditional social networks.
To study the statistical level of cohesiveness we calculate the clustering coefficient C according to [23], and also its weighted version Cw [1].
We notice no significant differences between them.
Thus the number of messages interchanged between two users is not relevant to determine the clustering level.
The impact of having a weighted network is analyzed in more detail in Section 2.5.
we associate to each user a score, which is calculated by averaging over all the scores of the comments of the same user.
This quantity allows us to differentiate high-quality writers (those with high mean score) from regular-quality writers.
The initial score of a comment is generally 1 if it comes from a registered user or 0 if it is anonymous3.
Moderation can modify the initial score to any integer within the range [−1, 5].
To ensure a representative subset of the network, we only consider users who wrote at least 10 comments, a total of 18, 476 users, representing approximately 23%.
Note that the minimum score is 0, since we eliminate −1 comments.
The distribution shows an unexpected bimodal profile, with two peaks at mean scores 1.1 and 2.3.
We take a simple approach based on agglomerative clustering which takes benefit from the weighted nature of the Slashdot network [18].
We choose the dense undirected network and start our procedure with each node as an independent cluster.
Let λ denote the number of comments, so that pairs of users (i, j) who interchange a number of comments wij ≥ λ are included in the network, and the other connections are discarded.
Starting from the biggest value λ = λmax and progressively decreasing it, users are connected incrementally and communities can be obtained.
This simple procedure is equivalent to building a dendrogram and allows to browse through the community structure at different scales by changing the parameter λ.
The vast majority of pairs of users only exchanges a small number of comments whereas a few of them really maintain intense dialogues during the year.
This seems to be the reason why previous properties such as the clustering coefficient do not show significant differences between the weighted and the unweighted network.
The most discussing pair of users exchanged a total of 108 comments.
We can see that the biggest component grows very fast and the second biggest remains small, showing evidence of a giant cluster present in all scales.
An initial picture of the activity generated by posts can be found in previous studies [12].
Posts receive on average approximately 195 comments and there exists a clear scale in the number of comments a post can originate.
Half of them receive less than 160 contributions.
A small number of highly discussed ones, however, can trigger more than one thousand contributions.
The number of comments gives an idea of how the participation is distributed among the different articles, but is not enough to quantify the degree of interaction.
For instance, a post may incite many readers to comment, but if the author of a comment does not reply the responses to his comment, there is no reciprocal communication within the thread.
In this case, although users can participate significantly, we can hardly interpret that the post has been highly discussed.
On the other hand, a post with a small number of contributors but with one long dialogue chain will evidence a high degree of reciprocal interaction (albeit its general interest may be reduced).
For deeper nesting levels, comments can be fully shown (score 4 or above), abbreviated (score between 1 and 4) or hidden (score below 1).
We propose a natural representation of thread discussions which takes advantage of their structure.
Consider a post as a central node.
Direct replies to this post are attached in a first nesting level and subsequent comments at increasing nesting levels in a way that the whole thread can be considered as a circular structure which grows radially from a central root during its lifetime, a radial tree.
Figure 7 shows three snapshots of a radial tree associated to a controversial post which attracted a lot of users.
An analog example of a less discussed post can be seen in Figure 8.
More examples of trees are shown in Figure 9.
Their profiles are highly heterogeneous.
In some examples, only a huge number of contributions without replies appear in the first level, resulting in trees with high widths but small depths.
In other examples, however, there are only discussions between two users who comment alternatively giving rise to very deep trees with small widths.
Sometimes, the intensity of the discussion is translated to one of the branches because of a controversial comment which triggers even more reactions than the original post (e.g the post in the center of Figure 9).
Apart from being a useful tool for browsing and examining the contents of a highly discussed post, radial trees can be used to describe statistically how information is structured in a thread.
In Figure 10a we plot the distribution of all the extracted comments per nesting level for all posts.
This gives an idea about the relation between the width versus the depth of the trees.
The first two levels contain most of the comments and then their number decays exponentially in function of the depth.
The maximum depth was 17.
It is important to note that a definition of controversial is necessarily subjective.
However, indicators such as the number of comments received or the maximum depth of the discussions can be, among others, good candidate quantities to evaluate the controversy of a post, but suffer from some drawbacks as we will explain in what follows.
We therefore seek for a measure, as simple as possible which incorporates as many of these factors and is able to rank a set of posts properly.
The number of comments alone does not tell us much about the structure of the discussion.
There might be a lot of comments in the first level but very little real discussions, such as in the post of Figure 12a.
A better measure for the controversy of a post seems to be the maximum depth of the nesting.
But again that measure has some drawbacks.
Two users may become entangled in some discussion without participation of the rest of the community, increasing the depth of the thread.
Unlike the BBS network [24, 8] where discussions are unrestricted, the scoring system of Slashdot guarantees a high quality and representativity of the social interaction.
This particular feature allowed us to find a correlation between scores and number of received replies and to distinguish clearly between two classes of users: good writers who, on average achieve high scores for their comments, and regular writers.
The number of replies of a comment depends mostly on its quality (the score it achieved) but we find some weak evidence for user reputation influencing the connectivity in the network.
Good writers are more likely than regular ones to receive replies to occasional comments with low scores.
However, this effect is not strong enough to cause assortative mixing by score since the opposite is not true.
Regular writers can expect a similar number of replies as good writers to their comments with high scores, so there is no negative effect of a user’s reputation.
Comments:
1. I think the author should be able to take the results compare with some of the practices in the community and do some combination, maybe can help us gain a better understanding of the meaning behind these phenomena.
2. Perhaps the author can do more explanation about the findings, this can also stimulate the feeling of the reader to the information.
Using Kolmogorov-Smirnov statistical tests, we show that the degree distributions are better explained by log-normal instead of power-law distri- butions.
We also study the structure of discussion threads using an intuitive radial tree representation.
Threads show strong heterogeneity and self-similarity throughout the dif- ferent nesting levels of a conversation.
We use these results to propose a simple measure to evaluate the degree of con- troversy provoked by a post.
To improve the quality and the representativity of the resulting graph, we filter some of the comments according to the following four criteria:
1.The post: Under this assumption, no relations exist between the post’s author and its direct commentators, unless he also participates later in the discussion.
2.Anonymous comments were also discarded.
3.We discard very low quality comments with score −1.
4.Finally, we filter out self-replies, often motivated by a forgotten aspect or error fix of the original comment.
The size of the big cluster for strongly connected components is of course, smaller.
gesting that the small-world property is present in all of them.
The quantities are approximately one unit lower than the corresponding value for a random graph ℓrand.
The maximal distance D between two users is also very small.
Even for the undirected sparse case, it only takes a maximum of eleven steps to reach a user starting randomly from any other.
These results are also in accordance with similar studies of other traditional social networks.
To study the statistical level of cohesiveness we calculate the clustering coefficient C according to [23], and also its weighted version Cw [1].
We notice no significant differences between them.
Thus the number of messages interchanged between two users is not relevant to determine the clustering level.
The impact of having a weighted network is analyzed in more detail in Section 2.5.
we associate to each user a score, which is calculated by averaging over all the scores of the comments of the same user.
This quantity allows us to differentiate high-quality writers (those with high mean score) from regular-quality writers.
The initial score of a comment is generally 1 if it comes from a registered user or 0 if it is anonymous3.
Moderation can modify the initial score to any integer within the range [−1, 5].
To ensure a representative subset of the network, we only consider users who wrote at least 10 comments, a total of 18, 476 users, representing approximately 23%.
Note that the minimum score is 0, since we eliminate −1 comments.
The distribution shows an unexpected bimodal profile, with two peaks at mean scores 1.1 and 2.3.
We take a simple approach based on agglomerative clustering which takes benefit from the weighted nature of the Slashdot network [18].
We choose the dense undirected network and start our procedure with each node as an independent cluster.
Let λ denote the number of comments, so that pairs of users (i, j) who interchange a number of comments wij ≥ λ are included in the network, and the other connections are discarded.
Starting from the biggest value λ = λmax and progressively decreasing it, users are connected incrementally and communities can be obtained.
This simple procedure is equivalent to building a dendrogram and allows to browse through the community structure at different scales by changing the parameter λ.
The vast majority of pairs of users only exchanges a small number of comments whereas a few of them really maintain intense dialogues during the year.
This seems to be the reason why previous properties such as the clustering coefficient do not show significant differences between the weighted and the unweighted network.
The most discussing pair of users exchanged a total of 108 comments.
We can see that the biggest component grows very fast and the second biggest remains small, showing evidence of a giant cluster present in all scales.
An initial picture of the activity generated by posts can be found in previous studies [12].
Posts receive on average approximately 195 comments and there exists a clear scale in the number of comments a post can originate.
Half of them receive less than 160 contributions.
A small number of highly discussed ones, however, can trigger more than one thousand contributions.
The number of comments gives an idea of how the participation is distributed among the different articles, but is not enough to quantify the degree of interaction.
For instance, a post may incite many readers to comment, but if the author of a comment does not reply the responses to his comment, there is no reciprocal communication within the thread.
In this case, although users can participate significantly, we can hardly interpret that the post has been highly discussed.
On the other hand, a post with a small number of contributors but with one long dialogue chain will evidence a high degree of reciprocal interaction (albeit its general interest may be reduced).
For deeper nesting levels, comments can be fully shown (score 4 or above), abbreviated (score between 1 and 4) or hidden (score below 1).
We propose a natural representation of thread discussions which takes advantage of their structure.
Consider a post as a central node.
Direct replies to this post are attached in a first nesting level and subsequent comments at increasing nesting levels in a way that the whole thread can be considered as a circular structure which grows radially from a central root during its lifetime, a radial tree.
Figure 7 shows three snapshots of a radial tree associated to a controversial post which attracted a lot of users.
An analog example of a less discussed post can be seen in Figure 8.
More examples of trees are shown in Figure 9.
Their profiles are highly heterogeneous.
In some examples, only a huge number of contributions without replies appear in the first level, resulting in trees with high widths but small depths.
In other examples, however, there are only discussions between two users who comment alternatively giving rise to very deep trees with small widths.
Sometimes, the intensity of the discussion is translated to one of the branches because of a controversial comment which triggers even more reactions than the original post (e.g the post in the center of Figure 9).
Apart from being a useful tool for browsing and examining the contents of a highly discussed post, radial trees can be used to describe statistically how information is structured in a thread.
In Figure 10a we plot the distribution of all the extracted comments per nesting level for all posts.
This gives an idea about the relation between the width versus the depth of the trees.
The first two levels contain most of the comments and then their number decays exponentially in function of the depth.
The maximum depth was 17.
It is important to note that a definition of controversial is necessarily subjective.
However, indicators such as the number of comments received or the maximum depth of the discussions can be, among others, good candidate quantities to evaluate the controversy of a post, but suffer from some drawbacks as we will explain in what follows.
We therefore seek for a measure, as simple as possible which incorporates as many of these factors and is able to rank a set of posts properly.
The number of comments alone does not tell us much about the structure of the discussion.
There might be a lot of comments in the first level but very little real discussions, such as in the post of Figure 12a.
A better measure for the controversy of a post seems to be the maximum depth of the nesting.
But again that measure has some drawbacks.
Two users may become entangled in some discussion without participation of the rest of the community, increasing the depth of the thread.
Unlike the BBS network [24, 8] where discussions are unrestricted, the scoring system of Slashdot guarantees a high quality and representativity of the social interaction.
This particular feature allowed us to find a correlation between scores and number of received replies and to distinguish clearly between two classes of users: good writers who, on average achieve high scores for their comments, and regular writers.
The number of replies of a comment depends mostly on its quality (the score it achieved) but we find some weak evidence for user reputation influencing the connectivity in the network.
Good writers are more likely than regular ones to receive replies to occasional comments with low scores.
However, this effect is not strong enough to cause assortative mixing by score since the opposite is not true.
Regular writers can expect a similar number of replies as good writers to their comments with high scores, so there is no negative effect of a user’s reputation.
Comments:
1. I think the author should be able to take the results compare with some of the practices in the community and do some combination, maybe can help us gain a better understanding of the meaning behind these phenomena.
2. Perhaps the author can do more explanation about the findings, this can also stimulate the feeling of the reader to the information.
2008年6月16日 星期一
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