Nguyen Thanh Phu 1820009
HUYNH LAB
Graduate School of Advanced Science and Technology Japan Advanced
Institute of Science and Technology
Knowledge Science
March, 2021
Research Content
Machine learning and data mining techniques have been developed
rapidly in recent times. In tasks such as classification, machine
learning techniques have been shown to equal to and even surpass
human performance. However, high-performance models are usually
complex, opaque and have low interpretability thus making it
difficult to explain the underlying behaviors of those models that
lead to the final outcomes. In many domains such as medicine and
health- care, interpretability is one of the most important factors
when considering the adoption of those models. In our research, we
aim to develop transparent machine learning models that are not
only able to provide users with knowledge about the underlying data
but also still can achieve competitive performance compared with
other commonly used techniques.
Specifically, in the field of unsupervised learning, clustering is
a fundamental task that has been utilized in many scientific
fields. Clustering groups data into clusters. For each cluster,
objects in the same cluster are similar between themselves and
dissimilar to objects in other clusters. K-means is a popular
interpretable method for the clustering task. However, it suffers
from the problem of underfitting data with simple dissimilarity
measures - a key part in formu- lating clusters of k-means. In our
work, we proposed a new k-means-based clustering method with a
novel dissimilarity measure that can better fit with the underlying
data. The effectiveness of the proposed clustering algorithm is
proven by a comparative study conducted on popular clustering
methods for categorical data.
In the field of supervised learning, we proposed a two-stage binary
classification system
Copyright c© 2021 by Nguyen Thanh Phu
i
named GSIC that is applicable for healthcare (or general) data.
GSIC benefits from a high level of interpretability and can at the
same time achieve the results comparable to commonly used
classification techniques. The motivation behind the proposed
system is the lack of effective classification methods for handling
data generated by various distributions (such as healthcare or
banking data) that can harmonize both performance and
interpretability perspectives. The experimental evaluation with a
use case in sepsis patients staying in ICU has shown the merits of
our proposed classification system.
On the other hand, we realized the limitation of our proposed
classification system when dealing with the uncertainty.
Specifically, real data with high uncertainty and ambiguity is
challenging for the classification task. E-KNN is a popular
evidence theory-based classification method developed for handling
uncertainty data. However, as a distance-based technique, it also
suffers from the problem of high dimensionality as well as mixed
distributed data where closed data points originated from different
classes. Based on that motivation, we enhanced our proposed
classification system GSIC with the capability of handling the
uncertainty existing in the underlying data. The classification
experiment conducted on various real data and popular classifiers
has shown that the proposed technique has competitive results
compared with state- of-the-art methods.
Research Purpose
Clustering is a common method that is widely used in a variety of
fields. Clustering groups data into clusters. For each cluster,
objects in the same cluster are similar between themselves and
dissimilar to objects in other clusters [Berkhin, 2006]. K-means
[Macqueen,1967] is the most well-known and widely used clustering
method. However, one inherent limitation of this approach is its
data type constraint, as the k-means technically can only work with
numerical data type. During the last decade or so, several attempts
have been made in order to remove the numeric data only limitation
of k-means to make it applicable to clustering for categorical
data. Particularly, some k-means like methods for categorical data
have been proposed such as k-modes [Huang,1998], k-representatives
[San et al., 2004], k-centers [Chen et al., 2013] and k-means like
clustering algorithm [Nguyen et al., 2016]. Although these
algorithms use a sim- ilar clustering fashion to the k-means
algorithm, they are different in defining cluster mean or
dissimilarity measure for categorical data.
Furthermore, measures to quantify the dissimilarity (similarity)
for categorical values are still not well-understood because there
is no coherent metric available between categorical values thus
far. Several methods have been proposed for encoding categorical
data as numerical values such as dummy coding (or indicator coding)
[Cohen, 1983]. Particularly, they use binary values to indicate
whether a categorical value is absent or present in a data record.
However, by treating each category as an independent variable in
that way, many important features and character- istics of
categorical data type such as the distribution of categories or
their relationships may not be taken into account. Especially, most
previous works have unfortunately neglected the semantic
information potentially inferred from relationships among
categories. In this research, we propose a new clustering algorithm
that is able to integrate those kinds of information into the
clustering process for categorical data. Specifically, the new
categorical clustering algorithm takes account of the semantic
relationships between categories into the dissimilarity measure.
Finally, an extensive experimental evaluation on benchmark data
sets from UCI Machine Learn- ing Repository has proved the
efficiency of our proposed algorithm with other existing
methods
Copyright c© 2021 by Nguyen Thanh Phu
ii
in term of clustering quality.
For the task of classification, applying deep learning techniques
could bring higher accuracy when dealing with big and heterogeneous
data. However, such high accuracy comes with high complexity and
opaqueness in the models [Johansson et al., 2011]. This situation
leads to the difficulty of interpretability of those models - one
of the important and required properties when implementing them
within a decision support system, especially in medicine,
healthcare and domains which require transparency for high-stake
decisions [Rudin, 2018]. Recently, there is an increase in the
popularity of explanatory artificial intelligence (XAI) or
relatedly interpretable ML. XAI allows the transparency in whole or
parts of systems and the explainability for the decisions from
them. According to [Gilpin et al., 2018], those explanations are
important to ensure algorithmic fairness, identify potential
bias/problems in the training data and ensure that the algorithms
perform as expected.
Due to the need for high-performance interpretable ML models,
especially for medicine and healthcare applications, we propose a
binary classifying system named GSIC (GSOM-based In- terpretable
Classifying System) that based on a systematic combination of
unsupervised and supervised ML techniques. In the proposed system,
GSOM (The Growing Self-Organizing Map) [Alahakoon et al., 2000]
plays a key role to help overcome the curse of dimensionality
problem as well as improve the efficiency and interpretability by
analyzing its generated mapping results. GSOM is selected as a
popular dimensional reduction and visualization method that has the
advantages of dynamically learning new data representation and
capability of revealing salient relations between objects from
underlying data contexts. In other to evaluate the performance of
our proposed system, an experiment on the classification task is
conducted. Furthermore, a use case on specific data of sepsis
patients in the Intensive Care Unit (ICU) is demonstrated in order
to prove the merit of our proposed system.
Besides, we realized the proposed classification system GSIC has
the limitation when dealing with the uncertainty data which can
degrade its performance. Specifically, the uncertainty can exist
inside data due to the lack of information (which some cases cause
the problem of sparse of dimensionality) or overlapping
(mix-distributed) data. As the problem of uncertainty cannot be
solved by traditional probabilistic framework [Jousselme et al.,
2003], Evidence theory [Shafer, 1976] is one popular solution that
can be applied to handle the uncertainty. In the field of
supervised learning, several approaches that adopt evidence theory
for solving the uncertainty problem. One of the notable methods is
EKNN [Denoeux, 1995] which classifies a new data point based on
evidence of classes of its neighbors which are discounted with the
distances be- tween them. Despite its robustness in dealing with
uncertainty information, EKNN suffers from several problems such as
computational complexity like any KNN-based methods which come from
the induction of distances in sample space. Several methods have
been proposed to rem- edy this limitation by applying feature
selection or dimensional reduction techniques in order to shrink
the feature spaces such as ConvNet-BF [Tong et al., 2019] or REK-NN
[Su et al., 2020].
Another inherent limitation of EKNN lays in its working mechanism
of selecting a fixed number of nearby neighbors to induce evidence
for classification process which can lead to mis- classifying due
to the lack of information or closed data points originating from
different classes. Also, the assignment of hard-to-classified data
to only an ignorance group is also debatable. In order to reduce
those limitations, instead of referencing to nearby neighbors,
several methods compare new data points with prototypes that are
produced from the training process such as ProDS [Denoeux et al.,
2000], CCR [Liu et al., 2014]. Other methods consider
assigning
Copyright c© 2021 by Nguyen Thanh Phu
iii
hard-to-classified data to a various classes-combined group named
meta-class in addition to the original ignorance class such as
BK-NN [Liu et al., 2013]. However, there are several criticisms
that evidence based on classes of prototypes is unreasonable due to
their non-semantic repre- sentations. Moreover, the classes of new
data points also have to be specified with a degree of certainty
rather than merely assigned to some common groups of classes.
In our work, we make an effort to remedy the above-mentioned
problems by proposing a new classification method that can induce
the evidence about the classes of new data objects based on groups
of data that belong to various distributions. Specifically, by
assuming that a data set contains instances that are generated by
several different distributions, data generated by each
distribution can be represented in the form of heterogeneous
clusters. Each distribution has different rules for characterizing
the classes of its generated data. For each cluster, we gauge
behaviors of the data distribution by using decision trees on the
whole set of data belonging to that cluster. Results from those
decision trees could be considered as evidence for determining the
classes of new data points. For making a final decision about the
predicted class, Demp- ster’s combination rule [Shafer, 1976] is
used to fuse the evidence collected from previous steps. Finally, a
classification experiment conducted on various real data and
popular classifiers has shown that the proposed technique has the
results comparable to state-of-the-art methods.
Research Accomplishment
• T.-P. Nguyen and V.-N. Huynh, “A New Classification Technique
Based on The Combination of Inner Evidence” in IUKM 2020:
Integrated Uncertainty in Knowledge Modelling, 2020, pp.
174-186.
• T.-P. Nguyen, S. Nguyen, D. Alahakoon and V.-N. Huynh, “GSIC: A
New Interpretable Sys- tem for Knowledge Exploration and
Classification” in IEEE Access, vol. 8, pp. 108544-108554, 2020,
doi: 10.1109/ACCESS.2020.3001428.
• T.-P. Nguyen and V.-N. Huynh, “A New Interpretable System for
Knowledge Exploration and Classification: ICU Sepsis Data Case
Study” in AHFE 2020: The Human Side of Service Engineering, July
2020.
• T.-P. Nguyen, D.-T. Dinh, and V.-N. Huynh, “A New Context-Based
Clustering Framework for Categorical Data” in PRICAI 2018: Trends
in Artificial Intelligence, 2018, pp. 697–709.
Copyright c© 2021 by Nguyen Thanh Phu
iv
A Study on Facility Location-allocation Models for Humanitarian
Relief Logistics
PRANEETPHOLKRANG PANCHALEE 1820028
Supervisor: Professor Huynh Van Nam
Graduate School of Advanced Science and Technology Japan Advanced
Institute of Science and Technology
[Knowledge Science]
March 2021
Part 1: Research Content
Nowadays, disaster onsets occur more frequently and take severe
impacts on humankind and economic systems across the world. When
disaster strikes, the relief agencies typically dispatch the relief
supplies to help the victims as well as rescue the victims from the
affected areas to the safe shelters. It can be stated that
decision-making on shelter location-allocation is the most critical
part of humanitarian relief logistics because it affects victims’
security and influences the success of disaster management
strategy. Without an appropriate approach for determining shelter
location-allocation, decision- makers would make ad-hoc decisions
which result in high cost, slow response, and failure in rescue the
victims. Proposing location-allocation models in the context of
humanitarian logistics, monetary criterion cannot be ignored
because it helps decision-makers to prepare sufficient budget in
response to disaster. In the same way, considering monetary and
non-monetary criteria simultaneously helps to ensure that the
victims are being taken care well under the optimal budget. Other
than model formulations, the proposed models should be solved by
proper approaches to generate optimal solutions. The victims and
decision-makers would get the benefit if the proposed models could
simplify prompt decision-making for determining location-allocation
in response to disasters. The aim of this research is to propose
the models and a novel solution to assist the decision-makers to
determine shelter location- allocation. Both monetary and
non-monetary criteria are taken into account in the proposed
models. The applicability of the proposed models is validated
through the real-word case study of shelter location-allocation in
response to flood in Surat Thani province of Thailand. The results
generated by the proposed models are evaluated with the current
shelter allocation plan determined by government sectors.
Part 2: Research Purpose
This study proposes the models to determine shelter
location-allocation in response to disaster. In addition to the
models, a novel approach for dealing with location-allocation is
proposed. Therein, four models are formulated to consider proper
locations to use as shelters. The first model seeks to deter- mine
shelter location-allocation with total cost minimization. The
proposed mathematical model is solved by Genetic Algorithm. The
second model
2
considers both monetary and non-monetary for justifying shelter
location- allocation. The objectives of the model are to
simultaneously minimize total cost, total evacuation time, and
number of open shelters. The proposed mathematical model is solved
by Epsilon Constraint method and Goal Pro- gramming which are the
posteriori and priori methods respectively. The third model seeks
to concurrently minimize total cost, and total evacuation time. The
proposed model is solved by a novel approach that integrated
Epsilon Constraint method and Artificial Neural Network to simplify
fast decision-making on shelter location-allocation. To the best of
our knowl- edge, there are no existing works that combined these
methods in coping with location problems, especially in field of
humanitarian relief logistics. The fourth model involves
multi-echelon relief facilities location-allocation. The first
echelon determines appropriate shelter location-allocation to mini-
mize total cost and minimize total evacuation time, while the
second echelon involves justifying distribution center
location-allocation to minimize distri- bution cost. The proposed
model is solved by Epsilon Constraint method. The applicability of
the proposed models and proposed solution approach is validated
through the case study of shelter location-allocation in response
to flooding in Surat Thani, Thailand. The results generated by each
model are compared with the current shelter location-allocation
plan determined by the government sector. The comparison results
indicate that consider- ing appropriate shelter location-allocation
based on proposed models mostly produces lower total cost than the
current plan with appropriate time frames for evacuating the
victims. It is plausible to use the proposed models and proposed
solution approach to improve shelter location-allocation in
response to disasters for the benefit of victims and
decision-makers.
Part 3: Research Accomplishment
The accomplishment of this study in aspects of theoretically and
practically in location-allocation problem in humanitarian
logistics can be demonstrated by the publications both
international journals and international conferences as
follows:
International Journal:
3
• Praneetpholkrang, P., Youji, K., Kanjanawattana, S., Huynh, V.N.
Two-Echelon Relief Facility Location-Allocation Model for
Humanitar- ian Supply Chain. Status: Plan to submit to
International Journal of Logistics Systems and Management.
International Conference:
• Praneetpholkrang, P., Huynh, V. N. (2020). Shelter Site Selection
and Allocation Model for Efficient Response to Humanitarian Relief
Logis- tics. The 7th International Conference on Dynamics in
Logistics, 12-14 February 2020, Bremen, Germany, in Dynamic in
Logistics, Lecture Notes in Logistics (pp. 309–318). Springer.
(peer review).
• Praneetpholkrang, P., Huynh, V. N., Kanjanawattana, S.
Bi-Objective Optimization Model for Determining Shelter
Location-Allocation in Humanitarian Relief Logistics. The 10th
International Conference on Operations Research and Enterprise
Systems, Online Streaming, 2-4 February 2021. (peer review).
Keywords: Facility Location-allocation, Relief Supply Chain,
Disaster Man- agement, Multi-objective Optimization, Epsilon
Constraint Method, Genetic Algorithm, Goal Programming, Artificial
Neural Network
4
Classroom: Association-based Activities, Biometric Data Analysis
and
Supportive Lighting Environment Exploration
Student Number: 1820031 Name: LIU TING
I. Research Content
Cultivating students’ creativity has become an important part of
teaching foreign languages at the university level. Foreign
language teachers need to think about curriculum design and
teaching approaches that can spark creativity in their students.
This study proposed a creative pedagogy for the foreign language
classroom. Activities that involve association and mind mapping in
a student-centered mode can encourage students to think creatively.
This study implemented association-based activities with mind
mapping to encourage students to exercise creative, divergent
thinking in their learning process. The setting for the study was a
school of Japanese studies at a university in Dalian city in China.
At this university, the students generally follow a traditional
curriculum, which is unconcerned with improving creativity. Our
fundamental aim was to explore whether a creative pedagogy could
effectively promote creativity development in students’ creative
thinking skills, language proficiency, and learning motivation. The
experimental group received an 8-week intervention that combined
the regular curriculum with association-based activities with mind
mapping. The control group received the regular curriculum. It
assumed that association-based activities with mind mapping
positively impact the cultivation of creativity.
At present, few studies have investigated to what extent
association-based activities influence foreign language learning
among university students in terms of creativity outcomes. To
clarify the effect of the association-based activities on
creativity, we employed an experimental methodology involving a
pre-test/post-test repeated measures design. All students were
tested on creativity performance using three assessment
instruments, a creative thinking test, a foreign language
proficiency test, and a motivation questionnaire: evaluating
creative thinking skills through creative thinking test,
performance rating by three factors of fluency, flexibility, and
originality; assessing Japanese language proficiency through
Japanese-language proficiency test,
in terms of vocabulary, reading comprehension, and writing;
administering a motivation questionnaire, including choice,
executive, and increased motivation questionnaire, to assess
students’ learning motivation.
Besides using traditional tests to measure students creativity
outcomes, an EEG investigation was taken for testing students’
divergent thinking skills, and an eye- tracking analysis was taken
for assessing students’ Japanese language proficiency, which
provided biometric data to further verify the effectiveness of
creative pedagogy. In recent years, with the rise and development
of cognitive neuroscience, the research techniques of
electroencephalography (EEG) and brain function imaging have
provided powerful research tools for directly observing the
activity of the brain when processing complex information, which
provides a more direct method for exploring the brain mechanism of
creative thinking, especially divergent thinking. In this study,
the brain wave images and data of the two groups students were
compared and analyzed during the divergent thinking tasks’ process.
It’s expected that the findings will deepen understanding and
promote the study of the effectiveness of creative thinking skills.
In addition, this study used eye tracking sensors to explore
creative pedagogy’s effects on reading ability that is considered
to be the comprehensive reflection of foreign language proficiency.
Eye tracking sensors was used to record eye movement indicators in
real time, going on to map the eye movement indicators to the
reading process that can effectively analyze the reading ability,
which provides a quantitative assessment and data evidence of
creative pedagogy’s effectiveness on students’ language
proficiency.
Moreover, besides teaching methods, providing suitable classroom
learning environments may further promote the cultivation of
creativity. This study explored the supportive classroom lighting
environment that can improve students’ participation in
association-based activities, so as to improve their creativity. No
literature was found to have explored the relationship of
association-based activities and classroom lighting environment in
the perspective of university students. The findings in this study
can be used as guidelines for designing psychology-oriented
classroom environments that can support the creativity cultivation
of students.
In summary, the findings in this study suggest that
association-based activities could be taken into consideration when
cultivating creativity in foreign language teaching in university,
and could be carried out in supportive classroom lighting
environment. Data and insights culled from the findings in this
study establish the knowledge framework of creative foreign
language teaching methods and evaluation, which will contribute to
the knowledge science to set future directions for the creative
pedagogy in the field of foreign language teaching and learning in
undergraduate education.
II. Research Purpose
The overall purpose of this study is to construct a new type of
foreign language classroom teaching method and learning environment
to achieve the teaching goal of promoting the development of
foreign language learners’ creativity, and to investigate what
extent the creativity could be cultivated. Through applying
association-based activities with mind mapping teaching method
design and conducting the supportive classroom lighting
environment, to explore the feasibility based on the analysis of
biometric data valuation, and suggest practical implication for
creative pedagogy design in the foreign language classroom.
Specific research objectives are as follows.
(1) Construction of a creative pedagogy of association-based
activities with mind mapping that centered on the development of
creativity.
(2) Clarification of the evaluation criterion of the
association-based activities. Evaluation comes from three aspects:
creative thinking skills, foreign language proficiency, and
learning motivation.
· Presenting traditional measurement methods for investigating the
association- based activities’ feasibility, including creative
thinking test, foreign language proficiency test, and learning
motivation questionnaire.
· Applying biometric data analysis of EEG investigation for
creative thinking skills, and eye-tracking detection for foreign
language proficiency to present more accurate numerical
results.
(3) Exploration the supportive lighting environments for students’
participating in association-based activities to further promote
their creativity.
This study takes “creativity is the inherent endowment of each
student” as it’s starting point, and therefore does not regard
creativity training as an additional teaching task in the process
of foreign language teaching, but rather believes that it can
promote learning motivation and improve the positive aspects of
foreign language expertise in the daily classroom. It is hoped that
the teaching methods and classroom learning environment as well as
the evaluation pattern that are presented in this study can be
extended to other foreign language education fields in colleges and
universities and promote the reform of foreign language
teaching.
III. Research Accomplishment
Papers published in journals
(1) Ting Liu, Takaya Yuizono; Mind Mapping Training’s Effects on
Reading Ability: Detection Based on Eye Tracking Sensors; Sensors;
20, 4422, 15 pages, 2020. (Doi:10.3390/s20164422; Indexed by
Scopus, SCI; Impact factor: 3.275; SJR Q1)
(2) Ting Liu, Takaya Yuizono, Zhisheng Wang, and Haiwen Gao; The
Influence of Classroom Illumination Environment on the Efficiency
of Foreign Language Learning; Applied Sciences; 10, 1901, 11 pages,
2020. (Doi:10.3390/app10061901; Indexed by Scopus, SCI; Impact
factor: 2.474; SJR Q2)
Conference proceedings
a. Ting Liu, Takaya Yuizono; Developing Innovation Skills in Second
Language Education-Cultivation of Creativity and Intercultural
Communicative Competence-; The 13th International Conference on
Knowledge, Information and Creativity Support Systems (KICSS-2018);
6 pages; November, 15-17, 2018, Pattaya, Thailand.
b. Ting Liu, Takaya Yuizono; Eye Movement Characteristics in
Reading Foreign Language Text Based on Mind Mapping Training; The
5th International Conference on Education (IICE Hawaii-2020) ; 1
page; January, 10-12, 2020, Honolulu, Hawaii, USA.
(2) Domestic conference proceeding
Ting Liu, Takaya Yuizono ; Proposal of Curriculum for Foreign
Language Education to Cultivate Creativity; The 40th Research
Conferences of Japan Creativity Society; accepted; 4 pages;
September, 11-13, 2018, Osaka, Japan.
Paper under review
Ting Liu, Takaya Yuizono; Association-based Activities Effects on
University Students’ Creativity in Foreign Language Classrooms;
2020, 09; Journal of Japan Creativity Society; 18 pages.
Oriented Development of Enterprise Message Management:
Study on Visual Attention of Email Topic Inference (AttLDA
for
Email) and Integration of ECS and ERP (SuccERP)
Intended Degree: Knowledge Science, Doctoral Degree
Laboratory: Nagai Lab
Student Number: 1820034
Research Content & Research Purpose
Our dissertation is mainly focusing on several topics for improving
collaboration
and communication in an enterprise. By considering two features of
collaboration,
unstructured collaboration (information collaboration) and
structured collaboration
(process collaboration), we primarily focus on two representative
tools: email and
Enterprise Resource Planning (ERP) System.
In terms of an enterprise, most of the current research result is
struggling to achieve
specific and practical goals by proposed theoretical findings in
the ERP domain. To
enable the managers to get a fuller picture of all the messages
generated from an ERP
system with the Enterprise Collaboration System (ECS) and improve
collaboration and
communication, we propose a complete method to develop an
artifact-SuccERP based
on the Design Science approach to carry out the integration. By
exploring multiple ERP
systems, we summarize our tasks into three aspects before
implementing the
integrations: authentication, data initialization, and specific
procedures implementation;
we also explain how the data-processing and integrations between
the ERP and ECS.
In our perspective, we can distinguish our contribution of the
proposed SuccERP
into two parts; 1) We present a complete demonstration of how to
get the architecture
and database schema of an existing ERP system and consider the
internal and external
hosting issues. 2) According to a series of literature reviews, we
implement the
integration based on the critical success factors and existing
issues presented in the
previous studies. In other words, we try to fill up the gap in
communication and
collaboration capabilities by enhancing the ERP and ECS systems'
integrations. In short,
we fulfill the data-processing and data-sharing from an ERP system
to the external
resources. Besides, based on our results, follow-up research can
explore the
implementation with other external resources for improving
different issues. Given the
context of the increasing demands of custom ERP, it is reasonable
to provide detailed
research as a guideline to those enterprises that plan to upgrade
and enhance their ERP
systems.
Next, the definition of information collaboration is employees
applying IT tools
to communicate and request assistance (answer); email is the most
standard
documentation tool for communication. Although existing studies use
the topic model
to support users for classifying emails, they disregard that human
is not like a machine
can focus on all the words in an email to determine the
distribution of email topics. The
Latent Dirichlet Allocation (LDA) model forms a basis for inferring
topics; our work
aims to discover how each word's visual attention influences the
topic inference and
estimates attention to a word according to its location
features.
By reviewing the visual-spatial research and the state-of-the-art
visual attention
models, we select the Bayesian Models to estimate attention and
proposing a novel
model-Attention orientation Latent Dirichlet Allocation model
(AttLDA). In AttLDA,
each email can regard as encoded into a two-dimensional space. We
take the line length
(the characters per line in an email) and window size (the number
of lines in an email)
into account to draw the optimal display size as a visual space and
assign a location for
each word in an email. Besides location, attention estimation also
considers the Term
Frequency and Inverse Document Frequency (TFIDF) and inferred
topics for each
iteration. Our aim is as follows; the readers can not completely
capture all the hidden
topics behind each word in an email, especially the context in the
forwarded message.
Unlike the previous research, our result shows each email's topic
distribution and
includes the distribution of related words' attention in each
topic. More precisely, we
can consider the visual attention as the significance of an email's
topic distribution. In
our experiment, we consider the public Enron email corpus as a
dataset and apply the
Perplexity metrics to measure the performance of AttLDA. AttLDA is
outperforming
the previous research on the perplexity evaluation.
Advanced technology has made the communication distance between
people
shorter than ever before and accumulates the number of messages
quicker and quicker.
People might quickly out of control for managing their messages
owing to their
negligence. Our research proposes the SuccERP, which builds a
platform to manage
ERP and ECS messages through definite guidelines to keep
communication efficiency.
On the other hand, we also proposed the AttLDA to effectively
extract the email topics
to improve email message management performance, and it can be
considered a feature
for settling further tasks.
Research Accomplishment
1. Lin, Y., Nagai, Y., Chiang, T., & Chiang, H. SuccERP: The
Design Science based
integration of ECS and ERP in post-implementation stage.
International Journal of
Engineering Business Management, Peer review, ijebm-20-0119.
(2nd-Review,
submitted at 25-Sep-2020).
2. Lin, Y., Nagai, Y., Chiang, T., & Chiang, H. AttLDA: Email
Topic Identification
using Latent Dirichlet Allocation integrated with Visual Attention.
Information
Processing and Management, Peer review, IPM-D-20-00545.
(1st-Review,
submitted at 10-July-2020).
3. Chiang, H. K., Nagai, Y., & Lin, Y. Y. (2020). Link up
Industry 4.0 with the
Enterprise Collaboration System to Help Small and Medium
Enterprises.
Mathematical Problems in Engineering, Peer review, vol. 2020, 1-13.
(Accept, I
am not first author).
4. Lin, Y., Nagai, Y., Chiang, T., & Chiang, H. (2020, March).
Design and Develop
Artifact for Integrating with ERP and ECS Based on Design Science.
In
Proceedings of the 2020 The 3rd International Conference on
Information Science
and System (pp. 218-223).
AI
AI
AI
AI
AI
4.2% AI 2020 :
5855
AI
AI
AI
A.
A-1.
Shirasaka , Hajime , Takashi Mikami , Makoto Onizuka, Youji Kohda
and Amna Javed” Structural
Condition of Combinatorial Innovation through Patent-ability AI
analysis”, International Journal of
Intellectual Property Management, Inderscience (2021 ).
A-2.
,2021 2 Vol.74 No.2 .
B.
Shirasaka , Hajime , Youji Kohda,2017,“Study on Impact of
Evaluation for Intellectual Property Value
using Artificial Intelligence on Intellectual Property Management”
5th International Conference on
Serviceology, Vienna: ICServ2017 , 207-210.
B-2.
Shirasaka , Hajime, Takashi Mikami, Youji Kohda, Amna Javed and
Yosuke Nara ,2019,“Artificial
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