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en: Introduction to Data Preprocessing in AI and Machine Learning --- Getting Started with Python

ja: AI・機械学習のためのデータ前処理 [入門編] --- Pythonでゼロからはじめる

en: Kagakujyoho Shuppan Co., Ltd.

ja: 科学情報出版

en: Around 2010, the term "Big Data" began to rapidly permeate the world around us. At the same time, the construction of intelligent AI systems based on machine learning from big data has even become a boom these days. This book focuses on the pre-processing of data, which is an inevitable part of building such machine learning systems, and although the primary focus is on machine learning systems such as AI, the various techniques described in this book can also be used effectively in data mining and data analysis to acquire useful knowledge from big data. However, the various techniques described in this book can also be used effectively in data mining and data analysis to obtain useful knowledge from big data.The term "big data" is pleasant to the ears and may sound like a treasure trove, but in fact big data is a mixture of cobbles and stones, a mixture of treasure and garbage. In fact, big data is a mixture of treasure and trash, and the larger the scale of the data, the more trash there is, and the more difficult it becomes to extract the treasure from it. In the field of computer science, there is a saying, "Garbage In, Garbage Out" (or GIGO for short). To build a good machine learning system, it is important to eliminate garbage from data and process the data into a form that is easy to learn, which is the essence of preprocessing.Recently, many frameworks and libraries for machine learning such as TensorFlow and Keras are available free of charge, and using these frameworks and libraries may seem to lower the threshold for programming. Using publicly available datasets for machine learning, anyone can easily create an AI system. However, there is a big problem when building your own system using your own data set. As is often said, real-world data is dirty. If you use the data as it is, that is exactly what you will get.If we use the data as it is, we will end up in a "Garbage In, Garbage Out" situation. Whether or not a useful AI system can be built from real data depends on the success of pre-processing. According to one theory, in the actual construction of AI and machine learning systems, 60-80% of the time spent by engineers is spent on data collection and preprocessing.This book focuses on preprocessing techniques, which have not been sufficiently covered in conventional books on machine learning, and aims to be a practical book where readers can understand the techniques not only by explaining them but also by actually running programs. This book has a sister volume, "Practical Edition", which is planned to be published in the near future. In "Practical Edition", more advanced preprocessing techniques and preprocessing techniques for media data such as text, images, sound, and music are explained.

ja: 2010年ごろを境に，ビッグデータという言葉が，わたしたちの身の回りに急速に浸透し始めた．同時に，ビッグデータから機械学習に基づき知的なAI システムを構築することが昨今のブームとさえなってきている．本書は，これらの機械学習システムを構築するうえで避けては通れない，データの前処理の部分を中心に解説している．AI などの機械学習システムを第一に想定しているが，本書で説明する各種の技術は，ビッグデータから有用な知識を獲得するデータマイニングやデータ分析などにも有効に用いることができる． さて，ビッグデータという言葉は耳に心地よく，なんとなく宝の山のように感じるかもしれないが，実はビッグデータは玉石混交であり，宝とゴミが入り混じっている．さらには，データの規模が大きくなればなるほどゴミも増え，その中から宝を取り出すのがますます困難になってくる．コンピュータサイエンスの分野では，``Garbage In, Garbage Out" (略してGIGO) という警句がある．文字通り，「ゴミからはゴミしか得られない」 「ゴミを入れればゴミが出てくる」 ことを意味しているが，この警句はまさに機械学習の一面を言い当てている．よい機械学習システムを構築するためには，データからゴミを排除し，データを学習しやすい形に加工するという作業が重要となってくるが，これこそがまさしく前処理の真髄である． 最近は，TensorFlow やKeras をはじめとする数多くの機械学習用のフレームワークやライブラリが無償で利用可能であり，これらのフレームワークやライブラリを利用することで，一見，プログラミングの敷居は低くなってきているようにみえる．公開されている機械学習用のデータセットを利用して，誰もが簡単にAI システムを作ることができる．しかし，独自のデータセットを用いて，独自のシステムを構築する場合には，大きな問題が立ちはだかっている．よくいわれていることであるが，現実のデータは汚い．データをそのまま使えば，それこそ ``Garbage In, Garbage Out" の事態に陥る．現実のデータから，有用なAI システムを構築できるかどうかは前処理の成否にかかっているとさえいえる．また，一説によると，実際のAI や機械学習システム構築の現場では，エンジニアが作業に携わる時間の6 割~ 8 割はデータの収集と前処理に費やされているともいわれている． 本書では，従来の機械学習の書籍では十分に扱われていなかった前処理技術に焦点をあて，技術の単なる解説だけではなく，実際に動くプログラムを通して，読者が理解できるような実践的な書を目指した．本書には姉妹編として 『実践編』 も出版が計画されているが，『実践編』ではより高度な前処理技術と，テキスト・画像・音響・音楽等のメディアデータに対する前処理技術について解説した．本書『 入門編』 とあわせてご活用いただきたい．


en: A Multimodal Control System for Autonomous Vehicles Using Speech, Gesture, and Gaze Recognition

en: Walter de Gruyter & Co.

ja: Walter de Gruyter & Co.

en: The development of autonomous vehicles has recently become the focus of much research. One area of investigation is the development of systems that will allow people to easily direct autonomous vehicles without any special technical skills or training. Human-machine interface technologies are likely to be used to achieve this objective. In this study, we propose an intuitive, multimodal interface system that uses speech, gesture, and gaze recognition to transmit user commands to the vehicle. We designed the multimodal understanding and dialog control components of the interface system separately, using finite-state transducers (FSTs), a technique which is also used to control conventional automated dialog systems. Our multimodal understanding and dialog control components can be thought of as a cascade of two separate transducers, but since cascading transducers can be combined into one transducer, our system is actually driven by a single FST. During trials, our proposed interface system allowed users to successfully direct and operate an autonomous vehicle using only speech, head movement, and gaze direction.


en: Design of Reminiscence Therapy System for Elderly People with Dementia

en: Springer

ja: Springer

en: 近年，認知症患者の数が増加し，介護者の負担が増大している．しかし，認知症の治療法は確立されておらず，その進行を制御することが治療の目的となっている．認知症の進行を抑制する1つの手段として， 介護者と認知症患者との対話による回想法を使用する．しかし，この方法では，介護者は何度も対話を行う必要があり，介護者にとって負担となるため，介護者の代わりにMMDAgentという音声対話システムを用いて回想法対話の支援を行う方法を提案する． これによって，介護者の負担が軽減される． 本稿では，提案されたシステムの設計を示す．


en: ここまで作れる! Raspberry Pi 実践サンプル集

ja: ここまで作れる! Raspberry Pi 実践サンプル集

en: マイナビ出版

ja: マイナビ出版


en: Integrating Symbols and Signals Based on Stream Reasoning and ROS

en: 実践的知能アプリケーション開発プラットフォームであるPRINTEPS(PRactical INTElligent aPplicationS)を開発した．本稿では，ストリーム推論とROS(Robot Operating System)を用いたイベント検出のためのPRINTEPSの応用と，知識処理による画像センシングの統合について述べる． このプラットフォームにおいて，我々はロボットカフェ内のロボットの動作をデモンストレーションし，ルールセットを変更することによって簡単にロボットカフェを実現・適用できることを示す．


en: Development and evaluation of spoken dialog systems with one or two agents through two domains

en: Almost all current spoken dialog systems treat dialog as that where a single user talks to an agent. On the other hand, we set out to investigate a multiparty dialog system that deals with two agents and a single user. We developed a three person (one user and two agents) and a two person (one user and one agent) dialog system to consider the same dialog tasks, that is, Which do you prefer, udon or ramen (Japanese noodle or Chinese noodle)? and Which do you want to travel to Hokkaido or Okinawa (snowy region or tropical region)? and compared them with respect to user behavior and satisfaction. According to the results of the experiments, the three person dialog system performed better in terms of lively conversation, and user can talk with the agents more like chatting.


en: A spoken dialog system for chat-like conversations considering response timing

en: If a dialog system can respond to a user as naturally as a human, the interaction will be smoother. In this research, we aim to develop a dialog system by emulating the human behavior in a chat-like dialog. In this paper, we developed a dialog system which could generate chat-like responses and their timing using a decision tree. The system could perform collaborative completion, aizuchi (back-channel) and so on. The decision tree utilized the pitch and the power contours of users utterance, recognition hypothesis, and response preparation status of the response generator, at every time segment as features to generate response timing.