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- 2. w ॴଐஜେֶത࢜લظ՝ఔ w /8(PPHMF1I%'FMMPXTIJQ w ࠷ۙͷझຯࣙԓͰ༡Ϳ w ͨ΄͍ݟग़͠ޠͷِͷҙຯΛߟ͑ͯὃ͠߹͏ήʔϜ w ʲ͜Γ͓ΒΜʳ ϥϯՊ২ͷҰͭɻ౦ೆΞδΞʹɻ ϕʔτʔϕϯͷۂָݭɻϑϥϯεͷఱจֶऀ Ֆ༄քͰɺͳ͔ͳ͔ؼΒͳ͍٬Λࢦ͢ޠɻ ઘڸՖʹΑΔখઆɻେਖ਼͔Βʰଠཅʱʹ࿈ࡌɻ w ୯७ʹؒͷݟग़͠ޠΛͯΔΫΠζͳͲָ͍͠ w ᅏ᳴ʢϋγϏϩίʣˠʓʓʓˠṟାʢ͠Ϳ͘Ζʣ ࣗݾհ !IJSPNV
- 3. w தֶʹೖͬͨͱ͖ʹϓϩάϥϛϯάΛ࢝ΊΔ w ॳɺٕڝϓϩάϥϛϯά-JOVYΧʔωϧͳͲʹ৮ΕΔɹ w ٕڝϓϩάϥϛϯάֶशࢧԉͷγεςϜ։ൃͰ தͷͱ͖ʹࠃͷະ౿ϓϩδΣΫτʹ࠾ w ߴߍͷؒηΩϡϦςΟʹͲͬΓ w $5'ͱݺΕΔίϯςετʹࢀՃͨ͠Γ w OESPJEͷηΩϡϦςΟΛ৮͍ͬͯΔ͏ͪʹ ւ֎ͷٕज़ຊͷमΛ͢Δ͜ͱʹͳͬͨΓ w େֶೖֶ͔ΒػցֶशͱώτͷؔΘΔྖҬͷڀݚ ུྺ %'$0/$5'
- 6. Robust Audio Adversarial Example for a Physical Attack Hiromu Yakura*†, Jun Sakuma*† * University of Tsukuba, Japan † RIKEN Center for Artificial Intelligence Project, Japan IJCAI 2020
- 7. എܠEWFSTBSJBMYBNQMFʹΑΔػցֶशͷ߈ܸ I. J. Goodfellow, et al. Explaining and harnessing adversarial examples. ICLR 2015. ͜͏͍ͬͨEWFSTBSJBMYBNQMFʹΑͬͯɺ ਓؒʹ͔ͮؾΕͣʹ*Λ༻͍ͨγεςϜΛѱ༻Ͱ͖Δ w σʔλʹҙਤతʹখ͞ͳϊΠζΛՃ͑Δ͜ͱͰɺ ػցֶशγεςϜΛؒҧΘͤΔ͜ͱ͕Ͱ͖Δ w ԼهͷྫͰɺը૾ྨγεςϜʹύϯμͷσʔλΛ ςφΨβϧͱೝࣝͤ͞Δ͜ͱ͕Ͱ͖Δ(PPEGFMMPX
- 8. എܠVEJPEWFSTBSJBMYBNQMFʹΑΔ߈ܸՄೳੑ N. Carlini, et al. Audio Adversarial Examples: Targeted Attacks on Speech-to-Text. IEEE DLS 2018. w 4JSJͳͲͷԻΞγελϯτ (PPHMF)PNFͳͲͷεϚʔτεϐʔΧ͕ීٴ w EWFSTBSJBMYBNQMFʹΑͬͯɺώτʹ͔ͮؾΕΔ͜ͱͳ͘ ԻೝࣝσόΠεʹ໋ྩͰ͖ΔՄೳੑ͋Δ w ը૾ͷ߹ͱҟͳΓɺςϨϏϥδΦͰ֦ࢄ͢Ε େྔͷσόΠεΛಉ࣌ʹ߈ܸ͢Δ͜ͱ͕Ͱ͖Δ ԻೝࣝϞσϧը૾ೝࣝΑΓෳࡶͳͨΊ ϊΠζͷӨڹ͋Δ࣮ੈքͰͷ߈ܸ͍͠$BSMJOJ
- 10. Generate (non-software) Bugs to Fool Classifiers Hiromu Yakura*†, Youhei Akimoto*†, Jun Sakuma*† * University of Tsukuba, Japan † RIKEN Center for Artificial Intelligence Project, Japan AAAI 2020
- 11. ఏҊώτʹո͠·Εͳ͍ࣗಈӡసंͷ߈ܸ S. Chen, et al. Shapeshifter: Robust physical adversarial attack on faster RCNN object detector. ECML PKDD 2018. w EWFSTBSJBMYBNQMFࣗಈӡసंͷ߈ܸͷϦεΫ͋Δ w ʮࢭ·ΕʯΛʮʯͱޡೝࣝͤ͞Ε͢Δ͔ w ͰࠨͷྫͷΑ͏ͳ༷ͩͱ͕͢͞ʹਓؒͮ͘ؾ [Chen+, '18] Proposed w ಛʹɺӡసதʹͨ͑ݟಓ࿏ඪࣝʹի͕ࢭ·͍ͬͯͯ ଟ͘ͷਓ͍͋͠ͱࢥΘͳ͍ͣ իͷΑ͏ͳεςοΧʔͰػցֶशϞσϧΛὃ͢͜ͱͰ ώτʹҧײΛ࣋ͨͤͣʹѱ༻Ͱ͖ΔՄೳੑΛࣔͨ͠
- 13. YES + STOP YES ؒҧ͑Δ͜ͱΛલఏʹͨ͠ώτͱػցֶशͱͷؔ࡞Γ͕ඞཁ A. Shafahi, et al.: Are adversarial examples inevitable?. ICLR 2019.
- 16. J. R. Zech, et al.: Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs. PLoS Medicine 15(e1002683). 2018.
- 17. FocusMusicRecommender: A System for Recommending Music to Listen to While Working Hiromu Yakura†, Tomoyasu Nakano‡, Masataka Goto‡ † University of Tsukuba, Japan ‡ AIST, Japan ACM IUI 2018
- 20. w ࡞ूʹۀத͢ΔʹඍົͳۂΛฉ͘΄͏͕Α͍ ػցֶशΛͬͯɺࣗʹͱͬͯඍົͳۂΛ୳ͯ͘͠ΕΔ ࡞༻ۀ#(.ָ͚ۂਪનγεςϜΛఏҊ R. Huang, et al.: Effects of background music on concentration of workers. Work 38(4). 2011. A. Londdale, et al.: Why do we listen to music? A uses and gratifications analysis. British Journal of Psychology 102(1). 2011.
- 21. ఏҊػցֶशΛ༻͍ͨ࡞༻ۀ#(.ਪનγεςϜ w ࠶ੜதͷεΩοϓ1$Ͱͷ࡞ۀϩάͱɺָۂͷԻڹใͰ ϢʔβͷᅂΛਪఆ͢ΔػցֶशϞσϧΛ࡞ w ࡞ۀதʹ༻͢ΔͨΊɺ໌ࣔతͳϑΟʔυόοΫͳ͠Ͱ Ϟσϧ͕͍ͯͬ͘ͳ͘ݡΠϯλϥΫγϣϯΛઃܭ Ұൠతͳָۂਪન ࡞༻ۀ#(.ͷਪન w ϢʔβͷϕετνϡʔϯΛ୳͢ w ඍົͳ͕ۂग़ͯ͘Δͱ ϢʔβΠϥοͱͯ͠͠·͏ w γεςϜͳ͠ͰɺࣗͰ ΞʔςΟετͳͲ͔Β୳ͤΔ w ͞Βͳͦ͞͏ͳۂΛ୳͢ w ଟগɺ͖ͳ͕ͯͬࠞ͟ۂ ผʹΠϥοͱ͠ͳ͍ w ͔͠ɺώτʹͱͬͯ ඍົͳۂΛ୳͢ͷ͍͠
- 22. Neural Malware Analysis with Attention Mechanism Hiromu Yakura*†, Shinnosuke Shinozaki*, Reon Nishimura*, Yoshihiro Oyama*, Jun Sakuma*† * University of Tsukuba, Japan † RIKEN Center for Artificial Intelligence Project, Japan ACM CODASPY 2018 / Computers and Security 2019
- 23. എܠϚϧΣΞͷڍಈղੳͷඞཁੑͱେม͞ w ใ࿙ӮͷௐࠪͰɺϚϧΣΞͷڍಈղੳ͕ඞཁͱͳΔ w ͲΜͳใ͕࿙Ε͔ͨΛͯ͢τϨʔεͤͶͳΒͳ͍ w ΞηϯϒϦΛߦ͍͔͚͍ͣͭͯ͘ඇৗʹେมͳ࡞ۀ σΟε ΞηϯϒϦ ਓखͰͷ ղੳ w ϚϧΣΞͷྨͱҟͳΓɺػցֶशͰͷࣗಈղੳ͍͠ w ͔͠ɺൈ͚͕͋ͬͯͳΒͳ͍ͷͰɺͲ͏ͯ͠ ઐՈ͕ղੳͨ͠ͱ͍͏୲อ͕ٻΊΒΕΔ ϚϧΣΞ ઐՈ Ͳ͜ʹͲΜͳใΛૹ͔ͬͨચ͍ग़͢
- 24. ఏҊػցֶशʹΑΔڍಈղੳͷώϯτఏڙ w ػցֶशʹΑΔࣗಈղੳΛࢦ͢ͷͰͳ͘ɺ ઐՈʹώϯτΛఏͯ͠ڙɺղੳΛޮԽ͢Δ w ϚϧΣΞͷྨख๏Λ֦ு͢Εɺ ػցֶशϞσϧ͕Ͳ͜ʹண͔ͨ͠ΛΔ͜ͱ͕Ͱ͖Δ w Ϟσϧ͕ண͔ͨ͠ΒղੳΛ࢝ΊΕ ޮతʹͦͷڍಈʹؔ࿈͢ΔՕॴΛḷΔ͜ͱ͕Ͱ͖Δ ྨͷࠜڌ ՕॴΛநग़ ղੳऀʹ ఏࣔ ࣗಈྨ Ϟσϧ ணͨ͠ՕॴΛϋΠϥΠτ ઐՈ ೖྗ ϚϧΣΞ
- 25. REsCUE: A framework for REal-time feedback on behavioral CUEs using multimodal anomaly detection Riku Arakawa† and Hiromu Yakura‡ (equal contribution) † The University of Tokyo, Japan ‡ University of Tsukuba, Japan ACM CHI 2019
- 26. എݱܠঢ়ͷձαϙʔτٕज़ͷݶք w ΧϯηϦϯάίʔνϯάͷձͰɺ ૬खͷඇޠݴγάφϧ͔ΒػඍΛಡΈऔΔ͜ͱ͕ٻΊΒΕΔ w ػցֶशΛ༻͍ͯձதͷಈ͖͔Β ૬खͷײΛಡΈऔΔͱ͍͏ख๏ఏҊ͞Ε͍ͯΔ w ͔͠͠ɺݱͰͦ͏ͨ͠ख๏͕ ʹཱͨͳ͍ͱݴΘΕΔ w ͦͦɺݸਓจ຺ʹΑͬͯ γάφϧͷҙຯ͕มΘͬͯ͘Δ w ͦͯ͠ײͱݧܦໃ६͢Δग़ྗΛ͢Δͱແࢹ͞Εͯ͠·͏ I. Damian, et al. Augmenting Social Interactions: Realtime Behavioural Feedback using Social Signal Processing Techniques. ACM CHI 2015. %BNJBO
- 27. ఏҊͱ؍அͷʹΑΔίϯϐϡʔλͱͷڠௐ w ίϯϐϡʔλ͕ྫྷ੩ͳࢹͰɺ૬खͷγάφϧΛ͚ͭݟग़͢ w γάφϧ͕Ͳ͏͍͏ҙຯΛ͔࣋ͭώτ͕அ͢Δ ώτ ίϯϐϡʔλ 👍ίϯςΩετΛཧղ͍ͯ͠Δ 👎ओ؍ਫ਼ਆతͳෛՙͷͨΊʹ ɹྫྷ੩ͳࢹͷҡ͕͍࣋͠ 👍ύϑΥʔϚϯεҰఆ 👎ίϯςΩετΛѻ͑ͳ͍ ͱ؍அΛͤ͞Ε ώτͱίϯϐϡʔλ͏·͘ڠௐͰ͖ΔͷͰͳ͍͔ʁ
- 29. Mindless Attractor: A False-Positive Resistant Intervention for Drawing Attention Using Auditory Perturbation ACM CHI 2021 Riku Arakawa† and Hiromu Yakura‡ (equal contribution) † The University of Tokyo, Japan ‡ University of Tsukuba, Japan
- 30. w ػցֶशΛ͑ɺإը૾͔Β ूத͍ͯ͠Δ͔Ͳ͏͔ΛਪఆͰ͖Δ w ΦϯϥΠϯतूʹۀத͍ͯ͠ͳ͍ੜెʹ γεςϜ͕ࣗಈͰΞϥʔτ͢Δͱ͍͏͜ͱՄೳʹ w ػցֶशͷݕޡͰΞϥʔτ͞Εͯ͠·ͬͨΒͲ͏ࢥ͏ʜʁ w ͦͦγεςϜʹʮूத͠ͳ͍͞ʯͱౖΒΕͨͱ͜ΖͰ ूத͢ΔͲ͜Ζ͔Δ͏·ͯͬ͠ͳ͘ͳ͕ؾՄೳੑ w ݕޡͷՄೳੑ͋ΔதͰҙΛଅ͢ʹͲ͏͢Εʁ എػܠցֶशʹΑΔूதͷηϯγϯά A. Gupta, et al. DAiSEE: Towards User Engagement Recognition in the Wild. arXiv. 2016. [Gupta+, '16]
- 33. w ػցֶशͰूத͍ͯ͠ͳ͍ͱஅ͞Εͨͱ͖ʹ ࣗಈͰͷେ͖͞ߴ͞Λඍົʹม͑ͯҙΛҾ͘ w มԽʹ͔ͨͬͳ͍͍ͯͮؾ߹ҙΛ͢ޮՌ͕͋ͬͨ ఏҊೝಛੑΛ͔ͨ͠׆Ϣʔβͷհೖख๏ R. J. Zatorre, et al. Neural specializations for speech and pitch: moving beyond the dichotomies. Philosophical Transactions of the Royal Society B, 363(1493), 2007. ώτձதɺ૬खͷҙΛҾͨ͘Ίʹ ແҙࣝతʹͷେ͖͞ߴ͞Λม͑Δ ݕޡ͕͋ͬͨͱͯ͠ΠϥΠϥͤͣ͞ʹ ޮՌΛൃ͢شΔհೖΞϓϩʔνΛఏࣔ
- 34. w ػցֶशٕज़ͷਫ਼Λ্͛Δ͜ͱॏཁ͕ͩ ಉ࣌ʹᘳͰͳͯ͑͘ΔΈΛ࡞Δ͜ͱ͕େ w ߈ܸͷՄೳੑؒҧ͏ϦεΫͲ͏ͯ͠ճආͰ͖ͳ͍ w ͜ͷΑ͏ʹώτͱίϯϐϡʔλͷΑΓΑ͍खͷऔΓ߹͍ํΛ ୳Δͷ͕)VNBO$PNQVUFS*OUFSBDUJPOͱ͍͏ֶ w ཧతഎܠͷཧղ͔ΒࣾձֶతͳٻͰ·؍ΊΒΕΔ ֶࡍతͰඇৗʹ໘ന͍ྖҬ w ࠷ۙग़ͨ͠จͰ$07*%Ͱ ѲखձΛͰ͖ͳ͍ΞΠυϧୡ͕ ͲͷΑ͏ʹίϯϐϡʔλγεςϜΛ͍ͬͯΔ͔Λੳ ·ͱΊ