Summarization of Scratch Input
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![Introduction
[ 背景 ]
1. コンピュータが持ち運び可能になってきた
2. しかし、少し距離を置いただけで触れなくなるのは不便である
[ 提案 ]
1. 既存な机や壁を入力装置に
2. 表面を指のツメでなぞった時になる音を入力にする
3. 新しいコンピュータと人間とのインタラクション](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-2-320.jpg)
![Sensing
[ 入力検知 ]
?ツメで木材や壁をなぞった時には、高い周波数がなるのを応用（大体
3000Hz 以上）
?人間の声は 90-300Hz 、歌で 800-1200Hz 、冷蔵庫や照明のノイズはせいぜ
い 50-60Hz
ちなみに、人間の可聴領域は、下は 20Hz 程度から、上は（個人差がある
が） 15,000Hz ないし 20,000Hz.](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-3-320.jpg)
![Sensing(2)
[ デバイス ]
?特殊な聴診器
増幅音や、高周波ノイズの検出に適しているため
聴診器を電気信号にするため、これに一般的なマイクをとりつけ
?信号は、オーディオ端子を通して PC に接続
大量生産する場合 1 ドル以下になる ( らしい )](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-4-320.jpg)
![Sensing(3)
[ 制限事項 ]
1. 入力の空間的な位置を把握できない
巧妙な音響指紋アプローチ ( ？ ) をすれば可能 [8]
関連研究としては、複数マイクを用いて位置検出を行っていた [10]
汎用性重視なので、そこは無視
2. 表面の材質が、きめ細かいものである必要がある
材質が細かいものの方が、音を効率よく生成しやすい
硬い材質というのは前提条件](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-5-320.jpg)
![Sensing(4)
[ やりたいこと ]
?壁、ドア、服、机、カウンター、キャビネット等、多くの表面に使用可能
したい
?貼付けるだけでセンシング可能に ( 設置が簡単 )
[ その他 ]
?また、二つの別々のテーブルは隣接されていも競合しない
それぞれが独立したタスクをこなせる
?尖ったものであれば、ツメでなくてもいける可能性がある](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-6-320.jpg)
![Gesture Recognition(2)
[ アルゴリズム ]
初期はダイナミック時間と、単純ベイズ分類器を用いた。
最終的には、ピークカウントと、振幅の変化に基づいた决定木を用いた。
モバイルデバイスで簡単に動き、リアルタイムに処理可能。
他には、ジェスチャー除去、ノイズ低減、振幅に依存しないピーク検出の手
法をいくつか使っている。](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-8-320.jpg)
![Gesture Recognition(3)
[ 検出できるもの ]
1. タップ
高周波と高強度の短い音のバーストによって特徴づけられる。
( ただ、どんなシステムでも出て来るが環境騒音がネックになる )
でも、なんか上手くいってるみたい ( 何故 !?)
2. スクラッチ入力
3kHZ 以上で動作させる事により、多くの問題 ( ノイズ等 ) を回避する。](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-9-320.jpg)
![Testing
[ テーブルに携帯を置いて実験 ]
木は緻密な素材なため、音を出しやすい
色んなテーブル (13 メートル、幅 1 ? 2 メートルの深さまで ) 、色んな場所
でテストしたけど、結果は良かった
ただし、二つ例外があり、ガラスと高光沢の木製テーブルは検出される信号
が弱すぎた](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-10-320.jpg)
![Testing(2)
[ テーブルに携帯を置いて実験 ]
センサはテーブルに置き、その上に適切な圧力がかかるように携帯を置いた
。
携帯が鳴ったときの応答として、ノーマルモード?サイレントモード ( 拒
否 ) ?アンサーモード ( 受け答える ) の 3 つをやったが、機械学習ベースの
エンジンはちゃんと反応した。
シンプルだけど、この実験は良かった。
机にデバイスが複数ある場合、ジェスチャーをブロードキャストしてみたい
とか考えていた。](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-11-320.jpg)
![Testing(3)
[ 壁をなぞる実験 ]
一般的な壁は乾式壁で、センサは 8m 感知できた。一つのセンサで、床から
天井までの 16m 、約 40 平方メートルの面積を認識できる。
コーナーによく届き、ドアの開口部は信号は弱くなるが一応届いた。
壁をダブルタップで再生 / 停止、ダブルスワイプでボリューム切り替えを行
った。
※ 適切な表面処理をせずにスクラッチを繰り返すと、傷がつく。](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-12-320.jpg)
![Testing(4)
[ 覆われたデバイスの実験 ]
多くのデバイスはプラスチック等で覆われている
Macbook Air を使って実験
1 個目 : タップすると、パワポのスライドをすすめたり
2 個目 : シングルタップでファイルやディレクトリを選び、ダブルタップで
開く
( ここの実験は、良くわからなかった )](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-13-320.jpg)
![Testing(5)
[ 布を使った実験 ]
モバイルはしばしばポケットにある。
シンプルなアクションのために一々取り出すのめんどくさいので、足を触っ
たら検知できるか実験した。
ポケットにセンサを入れて、左足にジェスチャーした場合、膝からベルトの
ラインまで検知できた。人間の抗生物質は多くが水なため、伝送特性が高い
ためだと考えられる。
ただ、右足や手は検知しなかった。あと歩いてるときのノイズ凄そう。](https://image.slidesharecdn.com/summarizationofscratchinput-130108020826-phpapp02/85/Summarization-of-Scratch-Input-14-320.jpg)
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Technology
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Hispanics may have a built-in defense against the isolation technology breeds, owing to their large, multigenerational households, strong social support systems, and tendency to use social media to stay connected with relatives abroad.
Age and Gender
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Race Affects Attitudes
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From their humble beginnings in 1984, TED has grown into the world’s most powerful amplifier for speakers and thought-leaders to share their ideas. They have over 2,400 filmed talks (not including the 30,000+ TEDx videos) freely available online, and have hosted over 17,500 events around the world.
With over one billion views in a year, it’s no wonder that so many speakers are looking to TED for ideas on how to share their message more effectively.
The article “5 Public-Speaking Tips TED Gives Its Speakers”, by Carmine Gallo for Forbes, gives speakers five practical ways to connect with their audience, and effectively share their ideas on stage.
Whether you are gearing up to get on a TED stage yourself, or just want to master the skills that so many of their speakers possess, these tips and quotes from Chris Anderson, the TED Talks Curator, will encourage you to make the most impactful impression on your audience.
See the full article and more summaries like this on SpeakerHub here: https://speakerhub.com/blog/5-presentation-tips-ted-gives-its-speakers
See the original article on Forbes here:
http://www.forbes.com/forbes/welcome/?toURL=http://www.forbes.com/sites/carminegallo/2016/05/06/5-public-speaking-tips-ted-gives-its-speakers/&refURL=&referrer=#5c07a8221d9bChatGPT and the Future of Work - Clark Boyd 
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Everyone is in agreement that ChatGPT (and other generative AI tools) will shape the future of work. Yet there is little consensus on exactly how, when, and to what extent this technology will change our world.
Businesses that extract maximum value from ChatGPT will use it as a collaborative tool for everything from brainstorming to technical maintenance.
For individuals, now is the time to pinpoint the skills the future professional will need to thrive in the AI age.
Check out this presentation to understand what ChatGPT is, how it will shape the future of work, and how you can prepare to take advantage. Getting into the tech field. what next 
Getting into the tech field. what next Tessa Mero
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The document provides career advice for getting into the tech field, including:
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- Contributing to open source projects to build experience and network.
- Developing a personal brand through a website and social media presence.
- Networking through events, communities, and finding a mentor.
- Practicing interviews through mock interviews and whiteboarding coding questions.Google's Just Not That Into You: Understanding Core Updates & Search Intent
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1. Core updates from Google periodically change how its algorithms assess and rank websites and pages. This can impact rankings through shifts in user intent, site quality issues being caught up to, world events influencing queries, and overhauls to search like the E-A-T framework.
2. There are many possible user intents beyond just transactional, navigational and informational. Identifying intent shifts is important during core updates. Sites may need to optimize for new intents through different content types and sections.
3. Responding effectively to core updates requires analyzing "before and after" data to understand changes, identifying new intents or page types, and ensuring content matches appropriate intents across video, images, knowledge graphs and more.How to have difficult conversations 
How to have difficult conversations Rajiv Jayarajah, MAppComm, ACC
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Stop putting off having difficult conversations. Seven practical tips to ensure your next difficult conversation go smoothly. Artificial Intelligence, Data and Competition – SCHREPEL – June 2024 OECD dis...
Artificial Intelligence, Data and Competition – SCHREPEL – June 2024 OECD dis...OECD Directorate for Financial and Enterprise Affairs
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Summarization of Scratch Input
- 1. Summarization of Scratch Input Yuki Anai
- 2. Introduction [ 背景 ] 1. コンピュータが持ち運び可能になってきた 2. しかし、少し距離を置いただけで触れなくなるのは不便である [ 提案 ] 1. 既存な机や壁を入力装置に 2. 表面を指のツメでなぞった時になる音を入力にする 3. 新しいコンピュータと人間とのインタラクション
- 3. Sensing [ 入力検知 ] ?ツメで木材や壁をなぞった時には、高い周波数がなるのを応用（大体 3000Hz 以上） ?人間の声は 90-300Hz 、歌で 800-1200Hz 、冷蔵庫や照明のノイズはせいぜ い 50-60Hz ちなみに、人間の可聴領域は、下は 20Hz 程度から、上は（個人差がある が） 15,000Hz ないし 20,000Hz.
- 4. Sensing(2) [ デバイス ] ?特殊な聴診器 増幅音や、高周波ノイズの検出に適しているため 聴診器を電気信号にするため、これに一般的なマイクをとりつけ ?信号は、オーディオ端子を通して PC に接続 大量生産する場合 1 ドル以下になる ( らしい )
- 5. Sensing(3) [ 制限事項 ] 1. 入力の空間的な位置を把握できない 巧妙な音響指紋アプローチ ( ？ ) をすれば可能 [8] 関連研究としては、複数マイクを用いて位置検出を行っていた [10] 汎用性重視なので、そこは無視 2. 表面の材質が、きめ細かいものである必要がある 材質が細かいものの方が、音を効率よく生成しやすい 硬い材質というのは前提条件
- 6. Sensing(4) [ やりたいこと ] ?壁、ドア、服、机、カウンター、キャビネット等、多くの表面に使用可能 したい ?貼付けるだけでセンシング可能に ( 設置が簡単 ) [ その他 ] ?また、二つの別々のテーブルは隣接されていも競合しない それぞれが独立したタスクをこなせる ?尖ったものであれば、ツメでなくてもいける可能性がある
- 7. Gesture Recognition ?一番シンプルな認識方法 指を動かす加速度による検知 具体的には、高周波、振幅 ( 音量 ) を用いる ?もうちょっと複雑な方法 ※ M/W 、 V/L 、 X/T は難しい ユニークな信号を元に認識
- 8. Gesture Recognition(2) [ アルゴリズム ] 初期はダイナミック時間と、単純ベイズ分類器を用いた。 最終的には、ピークカウントと、振幅の変化に基づいた决定木を用いた。 モバイルデバイスで簡単に動き、リアルタイムに処理可能。 他には、ジェスチャー除去、ノイズ低減、振幅に依存しないピーク検出の手 法をいくつか使っている。
- 9. Gesture Recognition(3) [ 検出できるもの ] 1. タップ 高周波と高強度の短い音のバーストによって特徴づけられる。 ( ただ、どんなシステムでも出て来るが環境騒音がネックになる ) でも、なんか上手くいってるみたい ( 何故 !?) 2. スクラッチ入力 3kHZ 以上で動作させる事により、多くの問題 ( ノイズ等 ) を回避する。
- 10. Testing [ テーブルに携帯を置いて実験 ] 木は緻密な素材なため、音を出しやすい 色んなテーブル (13 メートル、幅 1 ? 2 メートルの深さまで ) 、色んな場所 でテストしたけど、結果は良かった ただし、二つ例外があり、ガラスと高光沢の木製テーブルは検出される信号 が弱すぎた
- 11. Testing(2) [ テーブルに携帯を置いて実験 ] センサはテーブルに置き、その上に適切な圧力がかかるように携帯を置いた 。 携帯が鳴ったときの応答として、ノーマルモード?サイレントモード ( 拒 否 ) ?アンサーモード ( 受け答える ) の 3 つをやったが、機械学習ベースの エンジンはちゃんと反応した。 シンプルだけど、この実験は良かった。 机にデバイスが複数ある場合、ジェスチャーをブロードキャストしてみたい とか考えていた。
- 12. Testing(3) [ 壁をなぞる実験 ] 一般的な壁は乾式壁で、センサは 8m 感知できた。一つのセンサで、床から 天井までの 16m 、約 40 平方メートルの面積を認識できる。 コーナーによく届き、ドアの開口部は信号は弱くなるが一応届いた。 壁をダブルタップで再生 / 停止、ダブルスワイプでボリューム切り替えを行 った。 ※ 適切な表面処理をせずにスクラッチを繰り返すと、傷がつく。
- 13. Testing(4) [ 覆われたデバイスの実験 ] 多くのデバイスはプラスチック等で覆われている Macbook Air を使って実験 1 個目 : タップすると、パワポのスライドをすすめたり 2 個目 : シングルタップでファイルやディレクトリを選び、ダブルタップで 開く ( ここの実験は、良くわからなかった )
- 15. Evaluation 15 人 ( 女性 9 人 ) 、平均年齢 31 歳。 現実世界の入力面で実験するため、参 加者の家やオフィスで行った。 参加料として、 10 ドル払ったよ。 実験内容は、 6 つのシンプルなジェスチャーを選択してもらった だいたい 89.5% の成功率 ( 後者 4 つは難しかった ) 。
- 17. ふろく
- 18. ピークカウント
- 19. 决定木