Copyright (C) 2019 National Institute of Informatics, All rights reserved.

Copyright (C) 2019 National Institute of Informatics, All rights reser

Copyright (C) 2019 National Institute of Informatics, All rights reserved

Copyright (C) 2019 National Institute of Informatics, All rights reserved.

Copyright (C) 2019 National Institute of Informatics, All rights reserved.

Copyright (C) 2019 National Institute of Informatics. All rights reserv

ロ人出力

1. Introduction
Bitcoin was developed and released in 2009 in response to an inherent flaw in the way transactions were processed on the Internet. In his whitepaper, Nakamoto explains that “Commerce on the Internet has come to rely almost exclusively on financial institutions serving as trusted third parties to process electronic payments. While the system works well enough for most transactions, it still suffers from the inherent weaknesses of the trust based model” [1]. Since its original inception in 2009, Bitcoin has been rapidly adopted into today’s modern marketplaces. A primary issue with Bitcoin's rapid adoption is the increase of demand on the original blockchain to handle varying degrees of large transactions. With increased demand comes increased transactional waiting periods, and this has resulted in higher transactional fees in attempts to try and speed-up transaction confirmation times. The core innovation behind Bitcoin is its decentralized structure. Unlike traditional fiat currencies, Bitcoin has no central control, no central repository of information, no central management, and no central point of failure. However, one of the challenges facing Bitcoin is that most of the actual e-services and e-businesses built around the Bitcoin ecosystem are centralized. Due to the centralized nature of the current system, e-commerce is ran by individuals in specific locations that utilize vulnerable computer systems, that are susceptible to legal entanglements. Verge is one of the truly decentralized currencies available today due to its standing commitment to building off of the core fundamentals of Bitcoin, while bringing an entirely new layer of anonymity to realization. education program for top software engineers Jul 8 18:39  ●
Save Open D CandidateTerms.txt ~/repos/FeatureX/examples/2021-... Save in 1 'routing', 'communication', 'concealed', 'hop', 'tor', 'circuit', 'method', 'single point', 'communicating peers', 'be', 'determined', 'network surveillance', 'source', 'destination', 'ip integration ip', 'built', 'provide', 'hidden services', '
'people', 'host', 'servers', 'unknown locations', 'ip', 'many',
'same benefits', 'anonymous access', 'online', 'pp-style routing structure', 'layered', 'encryption', 'designed', 'network', 'internet', 'see', 'figure', 'traffic', 'contained', 'borders', 'performs', 'based', 'opposed', 's circuit based', 'benefit', 'route', 'congestion', 'service'
interruptions', 'manner', 'similar', 's ip', 'level', 'reliability', 'redundancy', 'first time', 'client', 'contact', 'query', 'distributed', 'network database', 'custom structured' 'distributed hash table', 'dht', 'kademlia', 'algorithm [ ]', 'done', 'find', 'other client', 'inbound tunnels', 'subsequent 'information', 'further network database lookups', data', 'required', 'obfuscated', 'service', 'ipv', 'verge', 'data', Plain Text  ▼  Tab Width: 8  ▼ Ln 1, Col 419 INS Plain text       Tab Width: 8 Ln 1, Col 1 INS RootFeature.txt /repos/FeatureX/examples/2021-... Save 1 Verge Plain Text         Tab Width: 8 Ln 1, Col 6 INS Top SE education program for top software engineers出力one-time public key
Managing the Machine

education program for top software engineers実際のマニュアルを対象にした際のFeatureXの実力を確認するためにキヤノン製品Canon i-SENSYS MF742Cdwの欧州向けユーザマニュアル(英語)に対してFeatureXによる解析を実行。 To reduce the various risks associated with the use of this machine, such as leaks of personal information or unauthorized use by third parties, constant and effective security measures are required. An administrator should manage important settings, such as access privileges and security settings, to ensure that the machine is used safely. ■ Configuring the Basic Management System ■ Preparing for Risks from Negligence or Misuse対象は機体管理の章・ (p.359-p456) ORestricting the Machine's Functions(P. 410) OIncreasing the Security of Documents(P. 424) ■ Ensuring Effective Management 3S21-06X Outline × / OSetting Access Privileges(P. 361) 359 410 424
結果：
education program for top software engineers ■ rootFeatureがmachineであることを特定。 ■ DFDのデータフローとプロセスに関連するものを関連付けれていた。例: 「password entered」と「login screen」の関連。問題点: ■ドメインエンジニア観点では、関連を示す線が不足している。 ■ネットワークのportがNetwork communicationと関連していない。同義語などがノードとして出現するので、ツリー構造が複雑化。考察： ■ドメインのFeature関連図を反映。 ■別のFeatureModelとの結合。 ■他の周辺機器のマニュアルの結果も考慮する。同義語をまとめる仕組みを考える必要がある。 ■辞書(手動で作ったリスト、MLを使って作るなど) education program for top software engineersまとめ■本ゼミでは、機械学習を用いて、要求仕様書から設計ドキュメント（特にデータの流れに着目したもの)を自動生成する研究について、ここ4年ほどの論文の調査を行った。自然言語で記述された要求仕様書から設計ドキュメントを自動生成する先行研究としては、ユースケース図・フィーチャーモデル・クラス図などを対象としたものがあり、それらの手法を適用することで、DFDの構成要素の抽出と要素の種類の識別が、ある程度の精度でできそうなことが判明した。論文の一つに、要求仕様書からフィーチャモデルを生成するツールを公開しているものがあったため試してみたが、日本語に未対応・コードの品質が悪い・同ーのエンティティがモデルの異なる場所に複数回現れる、といった問題があり、そのまま使うのは難しいことがわかった。 ■個別ゼミ2では、今回学んだ手法を活用することで、日本語で記述された要求仕様書からDFDを自動生成するツールの開発に取り組みたい。