Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing 2015

256 Pages·2016·11.265 MB·English

by  Roger Lee (eds.)

Studies in Computational Intelligence 612
Roger Lee Editor
Software Engineering,
Artificial Intelligence,
Networking and Parallel/
Distributed Computing
2015
Studies in Computational Intelligence
Volume 612
Series editor
Janusz Kacprzyk, Polish Academy of Sciences, Warsaw, Poland
e-mail: [email protected]
About this Series
The series “Studies in Computational Intelligence” (SCI) publishes new develop-
mentsandadvancesinthevariousareasofcomputationalintelligence—quicklyand
with a high quality. The intent is to cover the theory, applications, and design
methods of computational intelligence, as embedded in the fields of engineering,
computer science, physics and life sciences, as well as the methodologies behind
them. The series contains monographs, lecture notes and edited volumes in
computational intelligence spanning the areas of neural networks, connectionist
systems, genetic algorithms, evolutionary computation, artificial intelligence,
cellular automata, self-organizing systems, soft computing, fuzzy systems, and
hybrid intelligent systems. Of particular value to both the contributors and the
readership are the short publication timeframe and the worldwide distribution,
which enable both wide and rapid dissemination of research output.
More information about this series at http://www.springer.com/series/7092
Roger Lee
Editor
Software Engineering,
fi
Arti cial Intelligence,
Networking
and Parallel/Distributed
Computing 2015
123
Editor
RogerLee
Software Engineering andInformation
TechnologyInstitute
Central Michigan University
Mount Pleasant,MI
USA
ISSN 1860-949X ISSN 1860-9503 (electronic)
Studies in Computational Intelligence
ISBN978-3-319-23508-0 ISBN978-3-319-23509-7 (eBook)
DOI 10.1007/978-3-319-23509-7
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Foreword
The purpose of the 16th IEEE/ACIS International Conference on Software
Engineering, Artificial Intelligence, Networking and Parallel/Distributed
Computing (SNPD 2015) held on June 1–3, 2015 in Takamatsu, Japan, is aimed
at bringing together researchers and scientists, businessmen and entrepreneurs,
teachers and students to discuss the numerous fields of computer science, and to
share ideas and information in a meaningful way. This publication captures 17
oftheconference’smostpromisingpapers,andweimpatientlyawaittheimportant
contributions that we know these authors will bring to the field.
In chapter “On the Accelerated Convergence of Genetic Algorithm Using GPU
Parallel Operations”, Cheng-Chieh Li, Jung-Chun Liu, Chu-Hsing Lin, and
Winston Lo propose to accelerate the evolution speed of the genetic algorithm by
parallelcomputing,andoptimizeparallelgeneticalgorithmsbymethodssuchasthe
island model.
In chapter “A GPU-Based Pencil Beam Algorithm for Dose Calculations in
Proton Radiation Therapy”, Georgios Kalantzis, Theodora Leventouri, Hidenobu
Tachibana and Charles Shang conduct studies on Pencil-beam dose calculation
algorithmsforpro-tontherapythathavebeenwidelyutilizedinclinicalroutinefor
treatment planning purposes in most clinical settings, due to their simplicity of
calculation scheme and acceptable accuracy. The studies indicated a maximum
speedup factor of *127 in a homogeneous phantom.
Inchapter“IncrementalMax-MarginLearningforSemi-SupervisedMulti-Class
Problem”,Taocheng HuandJinhuiYuproposeanincrementalmax-marginmodel
for semi-supervised multi-classification learning, where efficiency and accuracy
need to be considered. Their approach captures essence of the exploration–
exploitation tradeoff.
In chapter “Improving Hypervisor Based SSD Caching with Logically
Partitioned Blocks and Scanning in Cloud Environment”, Hee Jung Park, Kyung
Tae Kim, Byungjun Lee, Rhee Man Kil and Hee Yong Youn propose a novel
hypervisor-based SSD caching scheme, employing a new metric to accurately
determine the demand on SSD cache space of each VM. Computer simulation
v
vi Foreword
confirms that it substantially improves the accuracy of cache space allocation
compared tothe existingschemes. It also allows to display comparable hit ratio as
the existing schemes with less amount of SSD cache for the VMs.
Inchapter“EmotionalSceneRetrievalfromLifelogVideosUsingEvolutionary
FeatureCreation”,HirokiNomiyaandTeruhisaHochinproposeanemotionalscene
retrieval framework for the purpose of promoting the utilization of a large amount
of lifelog videos. The proposed method is evaluated through an emotional scene
detection experiment using a lifelog video dataset containing spontaneous facial
expressions.
In chapter “On Solving the Container Problem in a Hypercube with Bit
Constraint”, Antoine Bossard and Keiichi Kaneko propose a routing algorithm
selectinginahypercubeinternallynode-disjointpathsbetweenanytwonodes,and
suchthattheselectedpathsallsatisfyagivenbitconstraint.Thecorrectnessofthe
proposedalgorithmisformallyestablishedandempiricalevaluationisconductedto
inspect the algorithm’s practical behaviour.
Inchapter“AlgorithmsforRemovingNodeOverlaps withSomeBasisNodes”,
Noboru Abe, Hiroaki Oh, and Kouhei Inoue propose three heuristic algorithms to
removenodeoverlapsingraphswithseveraltensofnodesbyrefiningapreviously
proposed algorithm, i.e., the force-transfer algorithm.
In chapter “Significant Frequency Range of Brain Wave Signals for
Authentication”, Preecha Tangkraingkij discusses a new biometric system using
brainwavesignals(EEG).Thepurposeofthisstudyistoexplorewhichfrequency
range of brain wave signals can be utilized for authentication.
In “Simple Models Characterizing the Cell Dwell Time with a Log-Normal
Distribution”,NaoshiSakamotopresentstwosimplemodelsinordertoestimatethe
probabilistic distribution of the cell dwell time. They show that the probabilistic
distribution of the cell dwell time of each model is approximated by a log-normal
distribution.
In chapter “A Method of Ridge Detection in Triangular Dissections Generated
byHomogeneousRectangularDissections”,KoichiAnada,TaiyouKikuchi,Shinji
Koka,YouzouMiyaderaandTakeoYakudiscussamethodfordetectionofridges
in 3D terrain maps. They introduce the steepest ascent method in triangular dis-
sections generated by homogeneous rectangular dissections.
In chapter “Architecture for Wide Area Appliance Management”, Arata Koike,
and Ryota Ishibashi studied architecture for Internet-of-Things (IoT) appliances
withconstrainedresourcestoenablecontrolandtomanageoverwideareanetwork.
They show realization of our proposed architecture by prototyping the system.
In chapter “Towards a Model Level Replication Technique for Fault Tolerant
SystemsUsingAADL”,WafaGabsiandBechirZalilaproposeanewtechniqueto
designreplicationusingtheAADLlanguageanditsextensibilitywithpropertysets.
We choose AADL to take advantage of its strong semantics at architecture level.
In chapter “Model Inference of Mobile Applications with Dynamic State
Abstraction”, Sebastien Salva and Patrice Laurencot and Stassia R. Zafimiharisoa
propose an automatic testing method of mobile applications, which also learns
formal models expressing navigational paths and application states.
Foreword vii
In chapter “Automatic Generation of S-LAM Descriptions from UML/MARTE
for the DSE of Massively Parallel Embedded Systems” Manel Ammar, Mouna
Baklouti,MaximePelcat,KarolDesnos,andMohamedAbidproposeatoolwhich
automatesthegenerationoftheSystem-LevelArchitectureModel(S-LAM)froma
UnifiedModelingLanguage-based(UML)modelannotatedwiththeModelingand
Analysis of Real-Time and Embedded Systems (MARTE) profile.
In chapter “Automatic Translation of OCL Meta-Level Constraints into Java
Meta-Programs” Sahar Kallel, Chouki Tibermacine, Bastien Tramoni, Christophe
Dony and Ahmed Hadj Kacem describe a system that generates metaprograms
starting from architecture constraints, written in OCL at the metamodel level, and
associatedtoaspecificUMLmodelofanapplication.Thesemetaprogramsenable
the checking of these constraints at runtime.
In chapter “Towards a Formal Model for Dynamic Networks Through
Refinement and Evolving Graphs” Faten Fakhfakh, Mohamed Tounsi, Ahmed
Hadj Kacem and Mohamed Mosbah propose a general and formal model for
dynamic networks based on evolving graphs and Event-B formal method. They
investigate an example of a distributed algorithm encoded by local computations
models.
In chapter “An Iterated Variable Neighborhood Descent Hyperheuristic for the
QuadraticMultipleKnapsackProblem”,TakwaTlili,HibaYahyaoui,andSaoussen
Krichen propose a hyper-heuristic approach based on the iterated variable neigh-
borhood descent algorithm for solving the QMKP. Numerical investigations based
on well-known benchmark instances. The results clearly demonstrate the good
performance of the proposed algorithm in solving the QMKP.
It isour sincere hope that this volume provides stimulation and inspiration, and
that it will be used as a foundation for works to come.
June 2015 Keizo Saisho
Kagawa University, Japan
Contents
On the Accelerated Convergence of Genetic Algorithm
Using GPU Parallel Operations . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
1
Cheng-Chieh Li, Jung-Chun Liu, Chu-Hsing Lin and Winston Lo
A GPU-Based Pencil Beam Algorithm for Dose Calculations
in Proton Radiation Therapy. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
17
Georgios Kalantzis, Theodora Leventouri, Hidenobu Tachibana
and Charles Shang
Incremental Max-Margin Learning for Semi-Supervised
Multi-Class Problem . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
31
Taocheng Hu and Jinhui Yu
Improving Hypervisor Based SSD Caching with Logically
Partitioned Blocks and Scanning in Cloud Environment . . . . . . . . . . . 45
Hee Jung Park, Kyung Tae Kim, Byungjun Lee,
Rhee Man Kil and Hee Yong Youn
Emotional Scene Retrieval from Lifelog Videos Using Evolutionary
Feature Creation. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
61
Hiroki Nomiya and Teruhisa Hochin
On Solving the Container Problem in a Hypercube
with Bit Constraint . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 77
Antoine Bossard and Keiichi Kaneko
Algorithms for Removing Node Overlaps with Some Basis Nodes . . . .
93
Noboru Abe, Hiroaki Oh and Kouhei Inoue
ix
x Contents
Significant Frequency Range of Brain Wave Signals
for Authentication. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
103
Preecha Tangkraingkij
Simple Models Characterizing the Cell Dwell Time
with a Log-Normal Distribution. . . . . . . . . . . . . . . . . . . . . . . . . . . . . 115
Naoshi Sakamoto
A Method of Ridge Detection in Triangular Dissections Generated
by Homogeneous Rectangular Dissections. . . . . . . . . . . . . . . . . . . . . .
131
Koichi Anada, Taiyou Kikuchi, Shinji Koka, Youzou Miyadera
and Takeo Yaku
Architecture for Wide Area Appliance Management. . . . . . . . . . . . . . 143
Arata Koike and Ryota Ishibashi
Towards a Model Level Replication Technique for Fault Tolerant
Systems Using AADL. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
159
Wafa Gabsi and Bechir Zalila
Model Inference of Mobile Applications with Dynamic State
Abstraction. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 177
Sébastien Salva, Patrice Laurençot and Stassia R. Zafimiharisoa
Automatic Generation of S-LAM Descriptions from UML/MARTE
for the DSE of Massively Parallel Embedded Systems. . . . . . . . . . . . .
195
Manel Ammar, Mouna Baklouti, Maxime Pelcat, Karol Desnos
and Mohamed Abid
Automatic Translation of OCL Meta-Level Constraints
into Java Meta-Programs. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
213
Sahar Kallel, Chouki Tibermacine, Bastien Tramoni, Christophe Dony
and Ahmed Hadj Kacem
Towards a Formal Model for Dynamic Networks
Through Refinement and Evolving Graphs. . . . . . . . . . . . . . . . . . . . .
227
Faten Fakhfakh, Mohamed Tounsi, Ahmed Hadj Kacem
and Mohamed Mosbah
An Iterated Variable Neighborhood Descent Hyperheuristic
for the Quadratic Multiple Knapsack Problem . . . . . . . . . . . . . . . . . .
245
Takwa Tlili, Hiba Yahyaoui and Saoussen Krichen
Author Index . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
253