Tardy Support by Hironori Fukamachi

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Presentation from TADHack Japan 13-14 February 2016 The third Telestax prize went to the hack Tardy Support by Hironori Fukamachi. Being late for work or school is stressful, on train you can not make a call. Instead you can have a call made on your behalf. This hack mashed up Telestax with Tropo.

Technology

RestCommを用いた
遅刻支援アプリケーション
2016/2/14 TADHack-mini Japan
アクロスウェイ株式会社システム開発部
深町公法

目次
• 自己紹介
• 背景
• 目的
• 機能概要
• システム構成
• アプリケーションのフロー
• デモ
• まとめ

自己紹介
• 会社
アクロスウェイ株式会社・・・電話システムの販売、構築、保守
• 業務
TROPOを利用したサービスの開発、導入、保守
（B2BUA、SMSゲートウェイ、課金サーバ等の構築とインフラ管理）

背景
１．遅刻する
２．遅刻連絡の電話をするのは嫌だ
３. ストレスの増加
４．１に戻る
ーー＞負の連鎖

目的
• 連絡はするけど、直接話さなくてよい
• 負の連鎖を断ち切る（ストレス軽減）
• 電車の中など、通話して会話ができない状況で有効

機能概要
• 着信を受けるとIVRが動作し、ボタンの押下で“遅れる時間（分）“を
入力する。
• 入力内容を確認すると、今月遅れた合計時間（分）をアナウンスし、
通話を切断する。
• 発信者のuser-info部からユーザ名と、ユーザに対応する連絡先を特
定する。連絡先は複数。
• ユーザに対応する連絡先へ発信する。
• 連絡先にはTTSで誰が何分遅れるかを伝え、必要ならばユーザへ
コールバックする。

システム構成
PSTN
RESTCOMM
TROPO
RDB
WEB
SIP Server B2BUA
User
Softphone
OperatorBoss
Media
Signaling

アプリケーションのフロー
AskTime ConfirmTime PressedOne
ReportLateti
me
ThankYou
CallBack
WEB
RDB
HTTP POST
JSON
HTTPS Response
HTTPS GET
Pressed Two
Pressed Two
Pressed One

IVRの内容
• AskTime
「私は遅刻のサポートをします。遅れる時間を分で入力してください。入力後＊を押し
てください。」
• ConfirmTime
「$lateminutesですね？よろしければ１を、再度入力する場合は２をおしてください。」
• PressedOne
「$lateminutes1分遅れです！今月は$totallatemin分遅れています。お気をつけて。」
• ReportLatetime
「こちらはRESTCOMMアプリです。$usernameさんは本日$lateminutes分遅れます。」
「$usernameさんを呼び出しますか？呼び出す場合は１、そうでない場合は２を押して
ください。」
• ThankYou
「ご了承ありがとうございます。」

作業内容
• 着信
• RESTAPIを使った発信
• 発信先との通話を別の発信先へつなぐ
• WEBサーバ、データベース等の連携
• 外部SIPサーバとの接続
• PSTNとの接続（SIPサーバのダイヤルプラン編集）

今後の展開
• WebUIを実装すれば使いやすくなる。（クリックツーコール等）
• 出退管理にも使えそう。
• 遅刻はしてはいけない。

Presented at TADHack Japan 13-14 Feb 2016. Emergency hack from Jinichi Ishizaki, Kyoko Minakuchi, Ryoju Ohata, Hiroki Kitabayashi, and Hiroki Maekawa with a mash-up of Telestax and Matrix to help get an emergency worker to a location, e.g. a hospital, as fast as possible. This was a great hack because it mashed up sponsor resources and showed clearly and powerful the impact it can have on solving real-world problems.

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我々はこれまで，新たなモノを産出する過程において「便利にすること」によって副次的に生じる課題を「便利の副作用」と定義し，その低減を目的としてアイディアの発想支援手法を提案してきた．これまでの研究では，便利前後の行為の増減に着目することにより便利の副作用への気づきの誘発が示唆されたものの，行為の増減の提示による便利の副作用への気づきへの影響は十分に検討できていなかった．そのため，本稿では行為の提示により便利の副作用に気づき，それを防いだアイディアの発想の支援が可能かの検証を目的として実験を行い，その有効性について検証する．実験では，行為の増減の提示の有無によりアイディア発想にどのような影響を与えるか検証を行う．

遺伝的アルゴリズムと知識蒸留による大規模言語モデル（LLM）の学習とハイパーパラメータ最適化

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オープンエンドな進化から着想を得て、個々の大規模言語モデル（LLM）が、グループとして学習を進めながら、ノームエージェントとして機能するという概念を探求しています。これは、単一のモデルでは難しい複雑な問題を解決することを目的としています。具体的な方法として、遺伝的アルゴリズムと知識蒸留を組み合わせた学習プロセスを提案しています。知識蒸留によって学習を進め、同時に遺伝的アルゴリズムでハイパーパラメータを最適化することで、より効率的な学習を目指します。ドメインタスクとして、指示からPythonコードを生成するコード生成タスクを選択しました。実験では、学習に3つの学習モデルと1つの教師モデルを使用しました。その結果、HumanEvalのpass@1で精度が1.2%向上し、学習が進むにつれて学習率が最適化された兆候が見られました。しかし、大幅な精度向上を達成し、さまざまなハイパーパラメータを最適化するには、まだ課題が残っています。

論文紹介：Deep Learning-Based Human Pose Estimation: A Survey

Toru Tamaki

生成AIがもたらすコンテンツ経済圏の新時代　　The New Era of Content Economy Brought by Generative AI

Osaka University

、コンテンツ制作の主体をプロのクリエーターからユーザーへと移行させている現状をご存じの方も多いでしょう。現在、画像コンテンツ制作現場では生成AIがクリエーターを支援するツールとして利用されています。セミナーでは、画像生成における自動化の境界についての議論が予定されています。 AIの利用には著作権や倫理的な問題も伴いますが、クリエーターとAIが共存し、センシング技術と統合することで新しい形のコンテンツが生み出されることが期待されています。

キンドリルネットワークアセスメントサービスご紹介今のネットワーク環境は大丈夫？調査〜対策までご支援します

Takayuki Nakayama

「進化するアプリイマ×ミライ～生成AIアプリへ続く道と新時代のアプリとは～」Interop24Tokyo APPS JAPAN B1-01講演

嶋　是一 (Yoshikazu SHIMA)