The 11th Hosei University IIST Colloquium
Master Thesis Report Session
第 11 回法政大学 IIST コロキウム
修了報告会
This colloquium is to introduce the IIST students’ research achievements.
Three graduating students will present their outstanding contributions tothe research fields of software security assurance, memory design, and plandisease diagnosis. All of the IIST students will attend the colloquium andjoin the discussion.

Date and Time: 09:30- 10:30 July. 14th, 2018
日時: 2018 年 7 月 14 日(土) 9:30-10:30
Venue: W103, Hosei University Koganei Campus
3-7-2 Kajiono cho, Koganei City, Tokyo 184-8584 JAPAN
会場: 法政大学,小金井キャンパス, 西館 1F W103

Host: Hosei University IIST: Institute of Integrated Science and Technology
Hosei University Global Education Center主催: 法政大学 IIST(総合理工学インスティテュート)
共催: 法政大学グローバル教育センター

PROGRAM
9:30-9:50 Huu Quan, CAP (2nd Year Master Student)
An End-to-End Practical Plant Disease Diagnosis System for Wide-Angle Cucumber
ImagesAbstract
With the breakthrough of deep learning techniques, many leaf-based automated plant
diagnosis methodologies have been proposed. As far as our best knowledge, most of
conventional methodologies only accept narrow range images, typically one or quite
limited number of target(s) are in their input. This is because appearance of leaf isdiverse and/or leaves are usually heavily overlap each other in practical. In this paper,
we propose a basic and practical end-to-end plant disease diagnosis system for wideangle images. Our system is principally composed of specially designed two types ofconvolutional neural network. The system achieves the leaf detection performance of
73.9% in F1-score, overall (detection and diagnosis) performance of 68.1% in recall and65.8% in precision at around 3 seconds/image on a total of 500 wide-angle on-site
images which have 6,860 healthy and 6,741 infected leaves (13,601 in total).
Huu Quan, CAP received the B.S degree from theUniversity of Information Technology, Vietnam NationalUniversity, Ho Chi Minh city, Viet Nam in 2016. He was aSAKURA Science exchanged student and came to Hosei
University in March 2016. After that program, he decided
to come back and continue to study at Hosei. He is now
second year IIST master student of Graduate School of
Science & Engineering in Hosei University, Koganei
Campus. His current research is Deep Learning, Image
Processing and applied it for agriculture. He is designing
an end-to-end practical plant disease diagnosis system thathelps farmers prevent disease and improve theirproductivity.

09:50-10:10 Peter Kimani Mungai (2nd Year Master Student)
Study on Chunking Mechanisms for a 3-Layered Associative Memory and Recall
Model
Abstract
Memory models are important components that support AI computational models tolearn and remember things. AI systems are expected to learn and improve withexperience just as the human brain does. Without an efficient associative memoryand recall system, the AI models cannot reliably perform cognitive tasks i.e. learn,store and remember. Usually, computers operate using an address-based memory forstorage where each memory item resides in a unique location. This study proposes a
3-layered associative memory model based on chunking mechanisms of the brain to
stores knowledge in form of association between entities. The human brain is acognitive model that derives information from sensory data like vision, auditory, andtouch, associates different patterns to create knowledge and uses chunking
mechanisms to package the acquired knowledge into manageable entities. The use of
chunking mechanisms by the brain aids it to overcome its short-term memory (STM)
capacity limitation. Through Chunking, each item held in the STM is a chunk containing
more associations (knowledge) in it. Using chunking mechanisms, AMR model canstore knowledge in manner that enables faster response to stimuli. To represent
knowledge and semantic relations effectively, the hyper structure (concept) definedin denotational mathematics is employed. For each memory item, its entities E,Properties A, internal relations 𝑹𝒊 , prior relations 𝑹≪ and post relations 𝑹≫ canbe rigorously be described. As such, during stimulation, a memory item can reveal allits internal and external semantic relations which is required by cognitive models toperform cognitive functions such as reasoning or deductions. A network of concepts
is maintained in a cognitive knowledge base that continually evolves as knowledge
accumulates. The chunking mechanisms used in this study are in tune with the mostvivid chunking mechanisms of the brain i.e. goal oriented chunking and automaticchunking.

Peter Mungai grew up in Nakuru, Kenya and aspired tobe a scientist. After graduating with a bachelor’s degree inKimathi University, he moved to Japan to pursue a master’s
degree in computer and information sciences. Throughout
his research work, he was actively involved in the activitiesof the AI lab and also published several research papers.
Beside academics, he enjoys swimming, reading andtravelling. His interests include cognitive computing,machine learning and entrepreneurship.

10:10-10:30 Busalire Onesmus Emeka (2nd Year Master Student)
Integrating Functional and Security Requirements Analysis using SOFL
for Software Security Assurance

AbstractFormal methods have been applied to define requirements for safety and/or security
critical software systems in some industrial sectors, but the challenge is the lack of asystematic way to take security issues into account in specifying the functional behaviors.
In this paper, we propose a formal approach to expressing and explicitly interweaving
security and functional requirements. With this approach, the functional behaviors of
the system are precisely specified using the Structured Object Oriented Formal
Language (SOFL), the security rules are systematically explored, and the result isproperly incorporated into the functional specification as constraints. The resultant
specification then defines the system functionality that implies the conformance to thesecurity rules. Such a specification can be used as a firm foundation for implementationand testing of the implementation. This approach aims at providing an interweaving
approach of specifying both functional and security system requirements thereby
eliminating the scenario where security requirements are elicited in an ad hoc manner.
We discuss the principle of interweaving security rules with functional specifications andpresent a case study to demonstrate the feasibility of our approach.

Busalire Onesmus Emeka received his BSc Computer
Science from Moi University, Kenya in 2012. Currently, heis a 2nd Year Masters student at Hosei University Graduate
School of Computer and Information Science, TokyoJapan. His current research is in Software Security with afocus on Requirement Specifications, especially for web
applications. He is working on a security requirement
engineering model that seeks to provide a weaving
approach where a software`s functional and security
requirement specifications can be defined together. Themodel shall enable software engineers give an early focus
on software security at the Requirement Specifications
phase of any Software Development Life Cycle . Emeka isalso a tech-entrepreneur and spends some of his time
researching andexploringfavorablemodels ofestablishing ecosystems for technology Startups.