Bridging the Local and the Global in DH: A Case Study in Japan

As the term “Digital Humanities” has been gradually gaining
attention around the world, with researchers in Englishspeaking
countries gathering under this banner in increasing
numbers. Among these, there are the earlier scholars who had
previously known the field as Humanities Computing; and there
are also scholars who have become involved more recently,
directly under the rubric of Digital Humanities. Yet another
new trend is that where scholars from non-English-speaking
and non-Western countries have also been gradually getting
involved in the international DH community. One of these recent
entrants to the international DH community is the Japanese
Association for Digital Humanities (JADH). The presence of this
new organization constitutes one piece of evidence to show that
the international community is gradually broadening the scope
of its membership. This trend has been actively supported by
the Multi-lingualism and Multi-culturalism Committee of the
ADHO, as well as by individual scholars who believe forming
a global community can only enrich DH and the humanities.
In view of this fact, it seems that it will become worthwhile to
release the CFP of the DH conference in many languages.
Additionally, recently several non-English Western
communities have been established. For example, Hispanic,
Italian, and German DH were discussed during the DH2012
conference at Hamburg. Each language area has long and
deep history to engage in the research and practice of DH.
Moreover, Global Outlook::Digital Humanities (GO::DH) has
started to cover a wider area, such as Latin America, China,
Africa, and so on, especially focusing on communication and
collaboration across and between High, Mid, and Low Income
Economies. It is remarkable that the first pilot project of the
GO::DH, “AroundDH in 80 Days” could immediately fill the list of
DH projects around the world (Gil) with the help of international
volunteers. In the context of the humanities, globalization is not
always intrinsically good, but international communication would
be significant for DH and the humanities.
While there have been efforts focused actual local
development, in some cases, such as of the Japan, most DH
activities hadn’t been known in the global community and
most of global DH activities hadn’t been known in Japan until
several years ago. This is in spite of the fact that the number
of identified Japanese DH-related researchers is over 200 and
recently the domestic annual DH conferences have gathered
40-60 papers every year, with 800 papers being presented
since 1989 from many universities, museums, libraries, and
other institutions in a DH-related quarterly workshop (A. Charles
Muller). As the case might be similar in other non-English and
non-Western countries, it might be useful to report our recent
attempts to bridge between the DH research being carried out
by non-English-speaking scholars and those in the international
community.
First, the establishment of the JADH has proved itself to be
one of the most effective solutions for closing this kind of gap.
Since around 80 researchers participated in the first conference
in 2011 in Osaka, 80-90 Japanese researchers have attended
the annual conference and communicated with international
researchers. Then, several germs of international collaboration
have come into being there and Japanese researchers
who paid attention to the results of research activities of the
international DH community have gradually increased as a
“methodological commons”—although most of the research is
still focused on Japanese or Eastern materials.
Secondly, an e-newsletter titled “Digital Humanities
Monthly” (DHII and ARG) has been published by the
International Institute for Digital Humanities since July 2011. It
is has 390 subscribers and is also published on the Web. The
e-newsletter written in Japanese consists of an invited essay,
brief news of international activities of DH and Digital History,
DH-event calendar, and reports of DH events held in Japan and
foreign countries collaborating with some local and international
voluntary DH researchers. The event reports are plotted on
a time-space map of the Neatline.(fig.1) The total number of
the access to the Web pages was over 5,000 this October.
According to comments of the readers, it seems to have gained
the attention of not only DH researchers, but also librarians,
curators, archivists, publishers, and general public, enabling
them to see the picture of the entire situation of the domestic
and the international DH.
Thirdly, we plan to make it easier to treat Japanese and
Eastern materials compliant with kinds of international
standards. So far, we are working to propose the encoding
of Han characters that occur in our research materials in the
Universal Character Set as a group of researchers (rather than
as a national body, as has been the policy heretofore) so that
researchers can not only treat the characters but also propose
the inclusion of new characters more easily. Moreover, we
are planning to form an appropriate guideline of text encoding
of Japanese and Eastern materials in the framework of the
Text Encoding Initiative P5 guidelines (Bauman) collaborating
with related researchers around the world. As a preparation
for this, we’ve held full-day TEI workshop by the participation
of international TEI researchers over 10 times and taught the
framework of the TEI to 50 researchers in total.
While it has up to now been difficult to bridge the local
and the global, we hope our attempts will be useful for an
Digital Humanities 2014
278
appropriate mode of globalization. We would like to discuss
various possibilities with participants in the conference.
Fig. 1:
References
A. Charles Muller, Kozaburo Hachimura, Shoichiro Hara,
Toshinobu Ogiso, Mitsuru Aida, Koichi Yasuoka, Ryo
Akama, Masahiro Shimoda, Tomoji Tabata, and Kiyonori
Nagasaki (2010). "The Origins and Current State of Digitization
of Humanities in Japan." Digital Humanities 2010 : 68-70.
Bauman, Lou Burnard and Syd (2007). TEI P5: Guidelines
for Electronic Text Encoding and Interchange. http://www.teic.org/release/doc/tei-p5-doc/en/html/index.html.
DHII and ARG. Ed. Kiyonori Nagasaki et al. 7 (2011). 
www.dhii.jp/DHM .
Gil, Alex ed. AroundDH | Global List. (2013). http://
goo.gl/4ov5nR.
Active Authentication through
Psychometrics
Noecker Jr, John
Juola & Associates, United States of America
What can your computer habits reveal about you?  The
answer might surprise you.  Previous work (Juola, et al., 2013)
has shown that just a few minutes of computer usage can be
used to identify who is at the keyboard and their demographic
and psychological attributes with a fairly high degree of
accuracy.  We expand upon this to show that the same usage
data can be used to thoroughly profile a previously-unknown
user to obtain valuable psychological information about the
user.
Authorship attribution, the analysis of a document’s writing
style to infer the author’s identity, is a well-established problem
in text classification.  Previously, we used classical authorship
attribution techniques to identify “who was at the keyboard”
using the DARPA Active Authentication Corpus (Juola et al,
2013).  Researchers have successfully applied the analysis
of language usage to infer authorship of written documents
(Juola, 2006. Koppel et al, 2009. Stamatatos, 2009. Jockers &
Witten, 2010), and stylometric analysis has also been applied
to things like gender (Argamon et al, 2006), personality (Luyckx
& Daelemans, 2008), and even psychological disorders like
depression (Rude et al, 2004).
Here, we attempt to perform the same technique with groups
composed of individuals who share common psychological
traits.  Previous work (Luyckx & Daelemans, 2008. Noecker
& Juola, 2013) on personality profiling has so far focused
on analyzing previously-written documents.  In contrast, our
system provides a method for real-time psychological profiling
of a user based on his or her interactions with a computer over
a relatively short period of time (approximately 30 minutes).
  The ultimate goal is two-fold: to learn something about a
previously-unobserved user (traditional stylometric identification
techniques require us to have training data on a user before
we can identify him) and to use psychological traits as an
enhancement to current user authentication methods.
Currently, exact accuracy on the user-based authentication
is approximately 90%.  This task becomes more difficult
(and the accuracy becomes correspondingly lower) as the
pool of potential author models grows.  In order to improve
overall accuracy of the user authentication task, we propose to
include these psychological profiling tools in the authentication
system.  If a given user can be identified as the most likely
candidate with 90% probability, and several facets of that user’s
personality can be confirmed with similarly high confidence,
this will increase the overall robustness of the authentication
system.
For our purposes, we used two personality/intelligence
measurement systems to profile users: Myers-Briggs Type
Indicator (MBTI) and Multiple Intelligences Developmental
Assessment Scales (MIDAS).
The Myers-Briggs type indicator (MBTI) assigns four binary
classifications to define personality (Myers & Myers, 1980)
–  Extroversion vs Introversion
–  iNtuition vs Sensing
–  Thinking vs Feeling
–  Judgement vs Perception
The Multiple Intelligences Developmental Assessment Scales
(MIDAS) were developed by Dr. Howard Gardner in his 1983
book “Frames of Mind” (Gardner, 1983).  He used a unique
definition of intelligence: “The ability to solve a problem or
create a product that is valued within one or more cultures” (MI
Research and Consulting).  He identified 8 primary intelligent
scales, each of which have several subscales (MI Research
and Consulting):
–   Musical
–   Vocal Ability
–   Instrumental Skill
–   Composer
–   Appreciation
–   Kinesthetic
–   Athletics
–   Dexterity
–   Logical-Mathematical
–   Everyday Math
–   School Math
–   Everyday Problem Solving
–   Strategy Games
–   Spatial
–   Space Awareness
–   Working with Objects
–   Artistic Design
–   Linguistic
–   Expressive Sensitivity
–   Rhetorical Skill
–   Written-academic
–   Interpersonal
–   Social Sensitivity
–   Social Persuasion
–   Interpersonal Work
–   Intrapersonal
–   Personal Knowledge / Efficacy
–   Effectiveness
–   Calculations
–   Spatial Problem Solving
–   Naturalist
–   Animal Care
–   Plant Care 
We also include a 9th main scale, Leadership, with its own
subscales: Communication, Management, and Social.
Materials and Methods
Lausanne, Switzerland
279
Corpus
In order to create the most accurate corpus possible, we
set up a simulated office environment and hired 80 temporary
workers for one week each.  Workers were tasked to perform a
long-term blogging project (research and write blog articles on
topics “related to Pittsburgh in some way”) over the course of a
normal workweek.  For this study, we use the Free Key Logger
output, which provides the exact text typed by each user.  We
do not include any information about the applications being
used or any data the user pastes from the clipboard.
Feature Extraction
For our analysis, we used the Java Graphical Authorship
Attribution Program (JGAAP) (Juola et al, 2009).  JGAAP
is a Java-based, modular program for textual analysis, text
categorization, and authorship attribution.  It provides a
comprehensive framework, allowing us to rapidly test the
effectiveness of different analysis techniques on the recorded
data.
JGAAP divides analysis into several steps: Canonicization
(Preprocessing), Event Set (Feature) Generation, and Analysis.
  In Canonicization, preprocessors are used to standardize
the text.  For this step, we converted all input letters to lower
case (“Unify Case”) and converted all strings of whitespace
characters into a single space character (“Normalize
Whitespace”).  At this stage, we also processed a variety of
special keyboard characters, converting these non-printable
characters into a printable placeholder (e.g. “backspace” was
replaced with “β”).  Finally, we divided the input data into blocks
of 1,000 characters, representing about 30 minutes of computer
usage.
For the event set generation, we tested character N-grams
for all N from 1 to 15, and word N-grams for N from 1 to 5.
  We then applied a number of analysis methods for each
experiment: Cosine Distance, Intersection Distance, Manhattan
Distance, and Matusita Distance.  For each method, we used
a centroid-based nearest neighbor classifier.  We performed
leave-one-out cross-validation to reach our final conclusion.
Models
For the MBTI classifiers, we built four binary classifiers (i.e. E
vs I, N vs S, T vs F, and J vs P).  For the MIDAS classifiers, we
first built a single 9-way classifier to identify a user’s principle
main scale.  This was the scale along which the user scored
highest (i.e. the scale for which the user showed the highest
preference).  For example, a user might have a preference
for “Musical” or “Linguistic”.  We also developed subscale
classifiers, which identify a user’s preference within each major
scale.  For instance, a user might be identified as “(Musical)
Vocal Ability” and “(Kinesthetic) Dexterity”, etc.  Thus, each user
was identified by a single main scale preference as well as nine
subscale preferences.
Results
MBTI
For the MBTI classifiers, we averaged an accuracy of 81.5%.
  The expected baseline average (assuming we pick the most
prevalent personality type for each category) is 55%. 
MIDAS
For the MIDAS main category identification, our best
performing classifier had accuracy of 70.7%.  This was using
character 15 grams with Intersection Distance.  The expected
baseline accuracy (achieved by choosing the most common
main scale, “Linguistic”) was 22.1%.
For the MIDAS subscale identification, the best performing
classifiers used a variety of Character n-grams, again with
Intersection Distance as the top performing analysis method. 
The average subscale accuracy was 81%.
Conclusion
We have shown here a method to reliably psychologically
profile a computer user based on only a short period (about
30 minutes) of usage time.  In addition to providing valuable
information about the user in question, this method can also
be used to provide additional layers of security for the active
authentication system we have described previously.  Even
in an adversarial situation, the difficulty of imitating both an
individual user’s style, as well as mimicking the psychological
profile of the user, will provide additional security to the
authentication system.
Also interesting to note is the limited usage data required to
perform these analyses.  The initial user psychological testing
period took approximately 3 hours, but accurate results were
obtained for only 30 minutes of computer usage.  In addition,
the three hours of testing were completely lost time – the
users were able to work only on the tests during this time.  In
contrast, the 30 minutes of analysis can be done on whatever
the user is working on at the time.  No downtime is required
to perform these analyses.  We believe this system could be
useful anywhere a non-intrusive analysis of a user might be
beneficial (e.g. determining whether a potential employee would
be a good fit).
For future work, we intend to focus on reducing the amount
of data needed even further.  Preliminary results on as little as
500 characters (about 15 minutes of usage time) have been
promising.  Additional work is also being done to integrate these
methods into the broader active authentication system in order
to bolster the overall reliability of the system.
References
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