Kyushu University Academic Staff Educational and Research Activities Database
Researcher information (To researchers) Need Help? How to update
Hirofumi Amano Last modified date:2021.08.03



Graduate School
Undergraduate School


E-Mail *Since the e-mail address is not displayed in Internet Explorer, please use another web browser:Google Chrome, safari.
Homepage
https://kyushu-u.pure.elsevier.com/en/persons/hirofumi-amano
 Reseacher Profiling Tool Kyushu University Pure
Phone
092-802-2646
Fax
092-802-2642
Academic Degree
Doctor of Engineering
Field of Specialization
Parallel Processing, Distributed Processing
Outline Activities
Amano's research interests include: single-level storage subsystems utilizing next-generation NVRAM; safe off-site backup methods utilizing storage virtualization techniques;
integration of modern electronic authentication techniques and conventional computer center service work flows.

With the help of the above research experiences, he gives a graduate-level course, "Advanced Program Design" featuring object-oriented software development and UML. In the undergraduate program, he gives a class of "Computer Systems II", discussing parallel and distributed processing.
Research
Research Interests
  • Application of Storage Virtualization Techniques for Research Data Management
    keyword : Storage Virtualization Techniques, Research Data Management
    2020.04.
  • Studies on Augmented Fairness Scheduling for General-Purpose Computing Environments
    keyword : Threads, Scheduling, Mult9-Core CPUs, Virtual Machines
    2016.04.
  • Studies on Parallel Stream Data Distribution Mechanisms for Coupling Multiple Parallel Programs on Overlay Clouds
    keyword : Overlay Cloud, Inter-Cloud
    2015.10~2019.03.
  • Studies on Single-Level Storage Subsystem Adaptable to Next-Generation NVRAM
    keyword : Single-Level Storage
    2015.09.
  • integration of modern electronic authentication techniques and conventional comuter center service tasks
    keyword : electronic authentication, computer center service tasks
    2011.04~2018.03.
  • Studies on Safe Off-Site Backup Methods Utilizing Storage Virtualization Techniques
    keyword : storage virtualization, off-site backup
    2010.04.
  • Job Submission Front-End for Grid Computing
    keyword : parallel processing, grid computing
    2000.04~2010.03job submission front-end for grid computing.
  • Remote Parallel Pipe Mechanism for Distributed Parallel Computing
    keyword : parallel processing, grid computing
    1998.04~2010.03remote parallel pipe mechanism for distributed parallel computing.
  • job scheduling for parallel supercomputers
    keyword : parallel processing, job scheduling
    1997.04~2002.03job scheduling for parallel supercomputers.
  • parallel input/output system for utilizing multiple disk drives
    keyword : parallel processing, parallel file system
    1994.04~1996.03parallel input/output system for utilizing multiple disk drives.
  • parallel database programming language for database applications
    keyword : parallel processing, parallel programming language, database, database programming language
    1991.04~2003.03parallel database programming language for database applications.
  • Pseudonatural Language Interface for Relational Databases
    keyword : Relational Database, User Interface
    1986.04~1993.03.
Academic Activities
Papers
1. Samih M. Mostafa, Hirofumi Amano, An Adjustable Variant of Round Robin Algorithm Based on Clustering Technique, Computers, Materials & Continua, doi:10.32604/cmc.2021.014675 , Vol. 66, No. 3, 3253-3270, 2020.12, CPU scheduling is the basic task within any time-shared operating system. One of the main goals of the researchers interested in CPU scheduling is minimizing time cost. Comparing between CPU scheduling algorithms is subject to some scheduling criteria (e.g., turnaround time, waiting time and number of context switches (NCS)). Scheduling policy is divided into preemptive and non-preemptive. Round Robin (RR) algorithm is the most common preemptive scheduling algorithm used in the time-shared operating systems. In this paper, the authors proposed a modified version of the RR algorithm, called dynamic time slice (DTS), to combine the advantageous of the low scheduling overhead of the RR and favor short process for the sake of minimizing time cost. Each process has a weight proportional to the weights of all processes. The process’s weight determines its time slice within the current period. The authors benefit from the clustering technique in grouping the processes that are similar in their attributes (e.g., CPU service
time, weight, allowed time slice (ATS), proportional burst time (PBT) and NCS). Each process in a cluster is assigned the average of the processes’ time slices in this cluster. A comparative study of six popular scheduling algorithms and the proposed approach on nine groups of processes vary in their attributes was performed and the evaluation was measured in terms of waiting and turnaround times, and NCS. The experiments showed that the proposed algorithm gives better results..
2. Samih M. Mostafa; Abdelrahman S. Eladimy; Safwat Hamad; Hirofumi Amano, CBRG: A Novel Algorithm for Handling Missing Data Using Bayesian Ridge Regression and Feature Selection Based on Gain Ratio, IEEE Access, 10.1109/ACCESS.2020.3042119, Volume 8, 216969-216985, 2020.12, [URL], Existing imputation methods may lead to biased predictions and decrease or increase the statistical influence which leads to improper estimations. Several missing value imputation approaches performance depends on the size of the dataset and the number of missing values within the dataset. In this work, the authors proposed a novel algorithm for manipulating missing data versus some common imputation approaches. The proposed algorithm imputes missing values in cumulative order depending on the gain ratio (GR) feature selection (to select the candidate feature to be manipulated) and the Bayesian Ridge Regression (BRR) technique (to build the predictive model). Each imputed feature will be used to manipulate the missing values in the following selected candidate feature. The proposed algorithm was implemented on eight different datasets after generating different missing values proportions from the missingness mechanisms. The imputation performance was calculated in terms of imputation time, mean absolute error (MAE), coefficient of determination ( R2 ), and root-mean-square error (RMSE). The results show the efficiency of the proposed algorithm when imputing any dataset with any number of missing data from any missingness mechanism..
3. Samih M. Mostafa, Abdelrahman S. Eladimy, Safwat Hamad, Hirofumi Amano, CBRL and CBRC: Novel Algorithms for Improving Missing Value Imputation Accuracy Based on Bayesian Ridge Regression, Symmetry, https://doi.org/10.3390/sym12101594, 12, 10, 2020.09, [URL], In most scientific studies such as data analysis, the existence of missing data is a critical problem, and selecting the appropriate approach to deal with missing data is a challenge. In this paper, the authors perform a fair comparative study of some practical imputation methods used for handling missing values against two proposed imputation algorithms. The proposed algorithms depend on the Bayesian Ridge technique under two different feature selection conditions. The proposed algorithms differ from the existing approaches in that they cumulate the imputed features; those imputed features will be incorporated within the Bayesian Ridge equation for predicting the missing values in the next incomplete selected feature. The authors applied the proposed algorithms on eight datasets with different amount of missing values created from different missingness mechanisms. The performance was measured in terms of imputation time, root-mean-square error (RMSE), coefficient of determination (R2), and mean absolute error (MAE). The results showed that the performance varies depending on missing values percentage, size of the dataset, and the missingness mechanism. In addition, the performance of the proposed methods is slightly better. .
4. Samih M. Mostafa, Hirofumi Amano, Dynamic Round Robin CPU Scheduling Algorithm Based on K-means Clustering Technique, Applied Sciences, https://doi.org/10.3390/app10155134, 10, 15, 2020.07, [URL], Minimizing time cost in time-shared operating system is the main aim of the researchers interested in CPU scheduling. CPU scheduling is the basic job within any operating system. Scheduling criteria (e.g., waiting time, turnaround time and number of context switches (NCS)) are used to compare CPU scheduling algorithms. Round robin (RR) is the most common preemptive scheduling policy used in time-shared operating systems. In this paper, a modified version of the RR algorithm is introduced to combine the advantageous of favor short process and low scheduling overhead of RR for the sake of minimizing average waiting time, turnaround time and NCS. The proposed work starts by clustering the processes into clusters where each cluster contains processes that are similar in attributes (e.g., CPU service period, weights and number of allocations to CPU). Every process in a cluster is assigned the same time slice depending on the weight of its cluster and its CPU service period. The authors performed comparative study of the proposed approach and popular scheduling algorithms on nine groups of processes vary in their attributes. The evaluation was measured in terms of waiting time, turnaround time, and NCS. The experiments showed that the proposed approach gives better results..
5. Samih M. Mostafa, Hirofumi Amano, Effect of Clustering Data in Improving Machine Learning Model Accuracy, Journal of Theoretical and Applied Information Technology, Vol. 97, No. 21, 2973-2981, 2019.11, [URL], Supervised machine learning algorithms consider the relationship between dependent and independent variables rather than the relationship between the instances. Machine learning algorithms try to learn the relationship between the input and output from the historical data in order to attain precise predictions about unseen future. Conventional foretelling algorithms are usually based on a model learned and trained from historical data. The instances in the historical data may vary in its characteristics. The variation may be a result of difference in case's pertinence degree to some cases compared to others. However, the problem with such machine learning algorithms is their dealing with the whole data without considering this variation. This paper presents a novel technique to the trained model to improve the prediction accuracy. The proposed method clusters the data using K-means clustering algorithm, and then applies the prediction algorithm to every cluster. The value of K which gives the highest accuracy is selected. The authors performed comparative study of the proposed technique and popular prediction methods namely Linear Regression, Ridge, Lasso, and Elastic. On analysing on five datasets with different sizes and different number of clusters, it was observed that the accuracy of the proposed technique is better from the point of view of Root Mean Square Error (RMSE), and coefficient of determination (R2)..
6. Samih M. Mostafa, Hirofumi Amano, An Adjustable Round Robin Scheduling Algorithm in Interactive Systems, Information Engineering Express, Vol. 5, No. 1, 11-18, 2019.05, [URL], CPU scheduling is considered as the basic job within the operating system. Scheduling criteria including waiting time, context switches and others have been suggested for comparing CPU scheduling algorithms. In this paper, a modified version of Round Robin algorithm is introduced as an attempt to combine the advantageous of low scheduling overhead of Round Robin and favor short process to minimize the average waiting time and number of context switches of running processes in interactive (time-shared) systems. A threshold is considered to determine whether the running process will be interrupted because of the expiration of its time slice specified by the Round Robin policy or will continue execution until termination. Derived results show that the suggested modification minimizes the average waiting time and number of context switches compared to Round Robin algorithm..
7. Samih M. Mostafa, Hirofumi Amano, Shigeru Kusakabe, Fairness and High Performance for Tasks in General Purpose Multicore Systems, International Journal of Research and Reviews in Applied Sciences, Volume 29, Issue 3, 74-86, 2016.12, [URL].
8. Samih Mohammed Mostafa, Shigeru Kusakabe, Hirofumi Amano, Fairness Scheduler for Multithreaded Programs in Virtual Machine Environment, The Fourth International Japan-Egypt Conference on Electronics, Communications and Computers, https://doi.org/10.1109/JEC-ECC.2016.7518972, 2016.05.
9. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, A Safe and Versatile Storage Server for Off-Site Backup, International Journal of Computer & Information Science (IJCIS), Volume 16, No. 1, 1-11, 2015.03, [URL], This paper makes a proposal for a safe and versatile storage server for off-site backup based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup copies. It also prevents a secret from leaking to others. The design of the prototype utilizing the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported. Future enhancement plans are also discussed..
10. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, An Approach to Safe Off-Site Backup Utilizing Secret Sharing Scheme and Storage Virtualization, Proceedings of the IIAI International Conference on Advanced Information Technologies 2013 (IIAI AIT 2013), 2013.11, This paper makes a proposal for a safe and versatile off-site backup system based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup data. It also prevents a secret from leaking to others. The design of the prototype based on the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported..
11. Eisaku Sakane, Kento Aida, Manabu Higashida, Taizo Kobayashi, Hirofumi Amano, Mutsumi Aoyagi, Grid Operational Supports for Middleware Deployment and User Administration, The International Symposium on Grids and Clouds and the Open Grid Forum - ISGC2011, 2011.03, [URL].
12. An Operation of the Inter-University Grid Infrastructure.
13. A. Makinouchi, H. Amano, T. Tsuji, K. Kaneko, Issues on Parallel Processing of Object Databases, Nontraditional Database Systems, pp. 231-248, published by Taylor & Francis, Inc., New York, 2002.08.
Presentations
1. Hirofumi Amano, Yuki Dohi, Hiromune Ikeda, An Approach to Safe Off-Site Backup Utilizing Secret Sharing Scheme and Storage Virtualization, IIAI International Conference on Advanced Information Technologies 2013 (IIAI AIT 2013), 2013.11, This paper makes a proposal for a safe and versatile off-site backup system based on secret sharing scheme and storage virtualization. This approach can save important data even after a disaster which destroys the primary data and some of its backup data. It also prevents a secret from leaking to others. The design of the prototype based on the (3, 5)-threshold secret sharing scheme and iSCSI is described, and preliminary experiment is reported..
Membership in Academic Society
  • IEEE (The Institute of Electrical and Electronics Engineers, Inc.)
Educational
Educational Activities
Amano teaches "Advanced Program Design" for graduate students in the Department of Advanced Information Technology.
He also teaches "Introductory Seminar on Electrical Engineering and Computer Science I" for freshmen (1st year) and "Computer Systems II A" and "Computer Systems II B" for juniors (3rd year) in the Department of Electrical Engineering and Computer Science.