[CB16] Keynote: How much security is too much? by Karsten NohlCODE BLUE
Based on one decade of impactful security research and several years as a risk manager, Karsten Nohl reflects upon what he would have done differently in pushing a data security agenda.
Our community is convinced that stellar IT security is paramount for companies large and small: We need security for system availability, for brand reputation, to prevent fraud, and to keep data private. But is more security always better?
Poorly chosen protection measures can have large externalities on the productivity, innovation capacity, and even happiness of organizations. Can too much security be worse than too little security?
This talk investigates the trade-off between security and innovation along several examples of current security research. It finds that some hacking research is counter-productive in bringing the most security to most people, by spreading fear too widely.
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Karsten Nohl
Karsten Nohl has spoken widely on security gaps since 2006. He and co-investigators have uncovered flaws in mobile communication, payment, and other widely-used infrastructures. In his work at an Asian 4G and digital services provider, and as Chief Scientist at Security Research Labs in Berlin, a risk management think tank specializing in emerging IT threats, Karsten challenges security assumptions in proprietary systems and is fascinated by the security-innovation trade-off. Hailing from the Rhineland, he studied electrical engineering in Heidelberg and earned a doctorate in 2008 from the University of Virginia.
[CB16] Keynote: How much security is too much? by Karsten NohlCODE BLUE
Based on one decade of impactful security research and several years as a risk manager, Karsten Nohl reflects upon what he would have done differently in pushing a data security agenda.
Our community is convinced that stellar IT security is paramount for companies large and small: We need security for system availability, for brand reputation, to prevent fraud, and to keep data private. But is more security always better?
Poorly chosen protection measures can have large externalities on the productivity, innovation capacity, and even happiness of organizations. Can too much security be worse than too little security?
This talk investigates the trade-off between security and innovation along several examples of current security research. It finds that some hacking research is counter-productive in bringing the most security to most people, by spreading fear too widely.
---
Karsten Nohl
Karsten Nohl has spoken widely on security gaps since 2006. He and co-investigators have uncovered flaws in mobile communication, payment, and other widely-used infrastructures. In his work at an Asian 4G and digital services provider, and as Chief Scientist at Security Research Labs in Berlin, a risk management think tank specializing in emerging IT threats, Karsten challenges security assumptions in proprietary systems and is fascinated by the security-innovation trade-off. Hailing from the Rhineland, he studied electrical engineering in Heidelberg and earned a doctorate in 2008 from the University of Virginia.
輪講用資料「Mitosis Detection in Breast Cancer Histology Images with Deep Neural Ne...Saya Katafuchi
2015年8月24日のゼミで使用した輪講用資料です
論文はDan C. Cires¸an, Alessandro Giusti, Luca M. Gambardella, J¨urgen Schmidhuber著「Mitosis Detection in Breast Cancer Histology Images
with Deep Neural Networks」です
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
輪講用資料「Mitosis Detection in Breast Cancer Histology Images with Deep Neural Ne...Saya Katafuchi
2015年8月24日のゼミで使用した輪講用資料です
論文はDan C. Cires¸an, Alessandro Giusti, Luca M. Gambardella, J¨urgen Schmidhuber著「Mitosis Detection in Breast Cancer Histology Images
with Deep Neural Networks」です
セル生産方式におけるロボットの活用には様々な問題があるが,その一つとして 3 体以上の物体の組み立てが挙げられる.一般に,複数物体を同時に組み立てる際は,対象の部品をそれぞれロボットアームまたは治具でそれぞれ独立に保持することで組み立てを遂行すると考えられる.ただし,この方法ではロボットアームや治具を部品数と同じ数だけ必要とし,部品数が多いほどコスト面や設置スペースの関係で無駄が多くなる.この課題に対して音𣷓らは組み立て対象物に働く接触力等の解析により,治具等で固定されていない対象物が組み立て作業中に運動しにくい状態となる条件を求めた.すなわち,環境中の非把持対象物のロバスト性を考慮して,組み立て作業条件を検討している.本研究ではこの方策に基づいて,複数物体の組み立て作業を単腕マニピュレータで実行することを目的とする.このとき,対象物のロバスト性を考慮することで,仮組状態の複数物体を同時に扱う手法を提案する.作業対象としてパイプジョイントの組み立てを挙げ,簡易な道具を用いることで単腕マニピュレータで複数物体を同時に把持できることを示す.さらに,作業成功率の向上のために RGB-D カメラを用いた物体の位置検出に基づくロボット制御及び動作計画を実装する.
This paper discusses assembly operations using a single manipulator and a parallel gripper to simultaneously
grasp multiple objects and hold the group of temporarily assembled objects. Multiple robots and jigs generally operate
assembly tasks by constraining the target objects mechanically or geometrically to prevent them from moving. It is
necessary to analyze the physical interaction between the objects for such constraints to achieve the tasks with a single
gripper. In this paper, we focus on assembling pipe joints as an example and discuss constraining the motion of the
objects. Our demonstration shows that a simple tool can facilitate holding multiple objects with a single gripper.
【DLゼミ】XFeat: Accelerated Features for Lightweight Image Matchingharmonylab
公開URL:https://arxiv.org/pdf/2404.19174
出典:Guilherme Potje, Felipe Cadar, Andre Araujo, Renato Martins, Erickson R. ascimento: XFeat: Accelerated Features for Lightweight Image Matching, Proceedings of the 2024 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) (2023)
概要:リソース効率に優れた特徴点マッチングのための軽量なアーキテクチャ「XFeat(Accelerated Features)」を提案します。手法は、局所的な特徴点の検出、抽出、マッチングのための畳み込みニューラルネットワークの基本的な設計を再検討します。特に、リソースが限られたデバイス向けに迅速かつ堅牢なアルゴリズムが必要とされるため、解像度を可能な限り高く保ちながら、ネットワークのチャネル数を制限します。さらに、スパース下でのマッチングを選択できる設計となっており、ナビゲーションやARなどのアプリケーションに適しています。XFeatは、高速かつ同等以上の精度を実現し、一般的なラップトップのCPU上でリアルタイムで動作します。