๋ณธ๋ฌธ ๋ฐ”๋กœ๊ฐ€๊ธฐ

๋ถ„๋ฅ˜ ์ „์ฒด๋ณด๊ธฐ31

[HCI] CHI ํ•™ํšŒ | HCI | LBW(Late-Breaking Work) CHI('์นด์ด')์˜ ํ’€๋„ค์ž„์€ ACM Conference on Human Factors in Copyting Systems๋กœ,์ธ๊ฐ„-์ปดํ“จํ„ฐ ์ƒํ˜ธ์ž‘์šฉ(HCI, Human-Computer Interaction) ๋ถ„์•ผ์—์„œ ๊ฐ€์žฅ ๊ถŒ์œ„ ์žˆ๋Š” ํ•™ํšŒ์ด์ž์ปดํ“จํ„ฐ ๊ณตํ•™ ๋ถ„์•ผ ํƒ‘ ์ปดํผ๋Ÿฐ์Šค๋‹ค.์—ฌ๊ธฐ์„œ ACM(Association for Computing Machinery)์€ ์ปดํ“จํ„ฐ ๊ณผํ•™ ๋ถ„์•ผ ํ•™ํšŒ ์—ฐํ•ฉ์ฒด๋‹ค.CHI ํ•™ํšŒ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™์€ ์—ฐ๊ตฌ ๋ถ„์•ผ๋ฅผ ๋‹ค๋ฃจ๊ณ  ์žˆ๋‹ค.์‚ฌ์šฉ์ž ๊ฒฝํ—˜ ๋ฐ ์‚ฌ์šฉ์„ฑ (User Experience and Usability)ํŠนํ™”๋œ ์ ์šฉ๋ถ„์•ผ (Specific Applications Areas)ํ•™์Šต, ๊ต์œก ๋ฐ ๊ฐ€์กฑ (Learning, Education, and Families)๊ฐœ์ธ์„ ๋„˜์–ด์„  ์ธํ„ฐ๋ž™์…˜ (Interaction .. 2023. 8. 30.
[HCI/Game] Cross hairs colors ์กฐ์ค€์„  ์ƒ‰์ƒ์„ ์ถ”์ฒœํ•ด์ฃผ๋Š” ์ฝ”๋“œ๋ฅผ ๋ฐœ๊ฒฌํ–ˆ๋‹ค. https://github.com/useewhynot/Valorant_Optimal_Crosshair GitHub - useewhynot/Valorant_Optimal_Crosshair Contribute to useewhynot/Valorant_Optimal_Crosshair development by creating an account on GitHub. github.com ์กฐ์ค€์„  ์ƒ‰์ƒ์€ ์•„๋งˆ๋„ ๋งต ์ƒ‰์ƒ๊ณผ ๋Œ€๋น„๋˜๊ณ  target ์ƒ‰์ƒ๊ณผ๋„ ๋Œ€๋น„๋ ์ˆ˜๋ก searching์— ๋„์›€์ด ๋  ๊ฒƒ์ด๋‹ค. ์ด ์ฝ”๋“œ๋Š” ๋งต๋งˆ๋‹ค average color๋ฅผ ์ธก์ •ํ•˜๊ณ  ์ด์— ๋Œ€๋น„๋˜๋Š” ์ƒ‰์œผ๋กœ ์กฐ์ค€์„  ์ƒ‰์ƒ์„ ์ถ”์ฒœํ•ด์ฃผ๋Š” ์ฝ”๋“œ๋‹ค. play ์˜์ƒ์—์„œ image๋ฅผ ์ถ”์ถœํ•ด ๋งต์˜ average color.. 2023. 8. 29.
[HCI/Graphics] Why would gamers use Low Graphics? / Visual Clutter in First-person shooter ์—ฌ๋ฆ„๋ฐฉํ•™ ๋™์•ˆ ๊ต์ˆ˜๋‹˜๊ณผ ํ•จ๊ป˜ ์—ฐ๊ตฌํ•˜์—ฌ SIGGRAPH’2023 ํ”„๋ก ํ‹ฐ์–ด์Šค ์›Œํฌ์ƒต์— ๋ฐœํ‘œํ•œ ๋‚ด์šฉ์ž…๋‹ˆ๋‹ค. ์ธํ„ฐ๋„ท์— ๊ฒŒ์ž„ ๊ทธ๋ž˜ํ”ฝ ์„ธํŒ…์„ ์กฐ๊ธˆ๋งŒ ์ฐพ์•„๋ณด์•„๋„ ์ˆ˜๋งŽ์€ ์œ ์ €๋“ค์ด Low quality setting์œผ๋กœ ํ”Œ๋ ˆ์ดํ•˜๊ณ ์ž ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ ์ด์œ ๋Š” ๋ฌด์—‡์ผ๊นŒ? 1. Trade-off with frame rates ์ปดํ“จํ„ฐ ์‚ฌ์–‘๋งˆ๋‹ค ๋‹ค๋ฅด๊ฒ ์ง€๋งŒ, ์ผ๋ฐ˜์ ์œผ๋กœ ๊ทธ๋ž˜ํ”ฝ์„ high quality๋กœ ์„ค์ •ํ•˜๋ฉด ์ดˆ๋‹น ๋ณผ ์ˆ˜ ์žˆ๋Š” frame์ด ์ ์–ด์ง„๋‹ค. ๋‹ค์‹œ ๋งํ•ด fps(frame per second)๊ฐ€ ๋–จ์–ด์งˆ ์ˆ˜ ์žˆ๋‹ค. ์ƒ๋Œ€๋ฐฉ๋ณด๋‹ค ๋Šฆ๊ฒŒ ์ƒ๋Œ€๋ฐฉ์„ ๋ฐœ๊ฒฌํ•œ๋‹ค๋Š” ๊ฒƒ์€ ๊ณง ํŒจ๋ฐฐ๋กœ ์ง๊ฒฐ๋  ์ˆ˜ ์žˆ๋Š” ๋ฌธ์ œ์ด๋‹ค. 2. Stretched targets ์ผ๋ถ€๋Ÿฌ ํ™”๋ฉด์„ ์ฐŒ๊ทธ๋ŸฌํŠธ๋ ค์„œ ์ (target)์ด ์ปค์ ธ ๋ณด์ด๊ฒŒ ํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๊ฒƒ์ด ์‹ค์ œ.. 2023. 8. 25.
[์˜ํ™”๋ฆฌ๋ทฐ] ๋ฌผ๋ฆฌํ•™์ž๋“ค์—๊ฒŒ ํ—Œ์ •ํ•˜๋Š” ์˜ํ™”, ์˜คํŽœํ•˜์ด๋จธ | ๋ณด๊ธฐ ์ „์— ์•Œ๊ณ  ๊ฐ€๋ฉด ์žฌ๋ฐŒ๋Š” ๊ณผํ•™ ์ด์•ผ๊ธฐ (์ŠคํฌO๋ผ๊ณ  ํ•˜๊ธฐ์—” ์ œ๋ชฉ์ด ์Šคํฌ์ž–์•„์š”) ์˜คํŽœํ•˜์ด๋จธ (โ˜…โ˜…โ˜…โ˜…โ˜…) ์žฅ๋ฅด: ์Šค๋ฆด๋Ÿฌ, ์ „๊ธฐ, ์ „์Ÿ, ๋“œ๋ผ๋งˆ, ์ •์น˜, ์‹œ๋Œ€๊ทน๊ฐ๋…, ๊ฐ๋ณธ: ํฌ๋ฆฌ์Šคํ† ํผ ๋†€๋ž€์ฃผ์—ฐ: ํ‚ฌ๋ฆฌ์–ธ ๋จธํ”ผ, ์—๋ฐ€๋ฆฌ ๋ธ”๋ŸฐํŠธ, ๋งท ๋ฐ์ด๋จผ, ๋กœ๋ฒ„ํŠธ ๋‹ค์šฐ๋‹ˆ ์ฃผ๋‹ˆ์–ด, ํ”Œ๋กœ๋ Œ์Šค ํ“จ    ์ง€๋‚œ์ฃผ ํ˜ผ์ž ์˜ํ™”๊ด€์— ๊ฐ€์„œ ์˜คํŽœํ•˜์ด๋จธ๋ฅผ ๋ดค์Šต๋‹ˆ๋‹ค.์ €๋Š” ํ•™์ฐฝ์‹œ์ ˆ ์–‘์ž์—ญํ•™์— ๊ด€ํ•œ ๊ณผํ•™ ์ด์•ผ๊ธฐ์— ๊ด€์‹ฌ์ด ๋งŽ์•˜๋Š”๋ฐ,๊ณต๋Œ€์— ์˜ค๊ณ  ๋‚˜์„œ๋Š” ์ด๋ก  ๋ฌผ๋ฆฌ๋‚˜ ํ™”ํ•™๊ณผ ๊ด€๋ จํ•ด ๊ณต๋ถ€ํ•  ์ผ์ด ์ „ํ˜€ ์—†๋”๋ผ๊ตฌ์š”. ๊ทธ๋Ÿฌ๋‹ค ์˜คํŽœํ•˜์ด๋จธ๋ฅผ ๋ณด๊ณ  ๋‚˜์„œ ์ œ ์•ˆ์— ๊ณผํ•™๊ณผ ๊ณผํ•™์‚ฌ๋ฅผ ์ข‹์•„ํ•˜๋˜ ๋งˆ์Œ์ด ๋‹ค์‹œ ๊ฟˆํ‹€๊ฑฐ๋ฆฌ๋Š” ๊ฒƒ ๊ฐ™์•˜์Šต๋‹ˆ๋‹ค.    ๐ŸŽž์‹œ๋†‰์‹œ์Šค๐ŸŽž  "๋‚˜๋Š” ์ด์ œ ์ฃฝ์Œ์ด์š”, ์„ธ์ƒ์˜ ํŒŒ๊ดด์ž๊ฐ€ ๋˜์—ˆ๋‹ค."์„ธ์ƒ์„ ๊ตฌํ•˜๊ธฐ ์œ„ํ•ด ์„ธ์ƒ์„ ํŒŒ๊ดดํ• ์ง€๋„ ๋ชจ๋ฅด๋Š” ์„ ํƒ์„ ํ•ด์•ผ ํ•˜๋Š” ์ฒœ์žฌ ๊ณผํ•™์ž์˜ ํ•ต ๊ฐœ๋ฐœ ํ”„๋กœ์ ํŠธ.   ๐Ÿ“์ค„๊ฑฐ๋ฆฌ๐Ÿ“ ์ด ์˜ํ™”๋Š” ์ •๋ง ์ž˜ ๋งŒ๋“  ์ „๊ธฐ ์˜ํ™”์ž…๋‹ˆ๋‹ค.. 2023. 8. 22.
[BOJ] ์ฝ”๋”ฉ ๋ฌธ์ œ ํ’€์ด๋Š” ๊นƒํ—ˆ๋ธŒ์—! ๊นƒํ—ˆ๋ธŒ ๋ธ”๋กœ๊ทธ์— TIS(Today I Solved)๋ฅผ ๋งŒ๋“ค์—ˆ์–ด์š”.์•ž์œผ๋กœ ์ฝ”๋”ฉ ๋ฌธ์ œ ํ’€์ด๋Š” ๊ฑฐ๊ธฐ์— ์˜ฌ๋ฆฌ๋ ค๊ณ  ํ•ด์š”!๊ทธ๋ฆฌ๊ณ  ๊ธ€๋กœ ํ’€์–ด์„œ ์ ์„ ๊ฐ€์น˜๊ฐ€ ์žˆ๋Š” ํŠน์ • ์•Œ๊ณ ๋ฆฌ์ฆ˜ ๋ฌธ์ œ๋“ค๋งŒ ํ‹ฐ์Šคํ† ๋ฆฌ์— ์ ์œผ๋ ค๊ณ  ํ•ฉ๋‹ˆ๋‹ค. ์•„๋ž˜๋Š” ์ œ ๊นƒํ—™ TIS ๋งํฌ์ž…๋‹ˆ๋‹ค. :) https://github.com/yoomimi/TIS GitHub - yoomimi/TIS: Today I Solved : ์ฝ”๋”ฉ ๋ฌธ์ œ ํ’€์ดToday I Solved : ์ฝ”๋”ฉ ๋ฌธ์ œ ํ’€์ด. Contribute to yoomimi/TIS development by creating an account on GitHub.github.com 2023. 8. 22.
[AI] 2023-2ํ•™๊ธฐ ๊ณต๋ถ€ํ•  ๋‚ด์šฉ ์š”์•ฝ [์ง„๋„] linear regression logistic regression decision tree ensemble learning (Bagging&Boosting) dimension reduction (PCA & LDA) neaural networks (Basics/Backpropagation/In pratice) CNNs(Convolutional Neaural Networks) clustering [์„ ์ˆ˜๊ณผ๋ชฉ] linear algebra, probability, calculus, optimization, data structure and Python [์ฐธ๊ณ ์ž๋ฃŒ] Pattern recognition and machine learning (C. Bishop) An introduction to statis.. 2023. 8. 14.
[BOJ] 3098๋ฒˆ. ์†Œ์…œ๋„คํŠธ์›Œํฌ (C++) ํ…Œ์ŠคํŠธ์ผ€์ด์Šค๋Š” ๋‹ค ๋งŒ์กฑํ•˜๋Š”๋ฐ ์ž๊พธ ํ‹€๋ ธ์Šต๋‹ˆ๋‹ค๊ฐ€ ๋– ์„œ ์งฌ์งฌ์ด 3์ผ์— ๊ฑฐ์ณ ๋งžํ˜€๋‚ธ ๋ฌธ์ œ..์ฒ˜์Œ์— ์ƒ๊ฐํ–ˆ๋˜ ๋ฐฉ์‹์œผ๋กœ ๊ตฌํ˜„ํ•˜๊ณ  ์‹ถ์€ ๊ณ ์ง‘ ๋•Œ๋ฌธ์— ์‹œ๊ฐ„์„ ๋งŽ์ด ๋‚ ๋ ธ๋‹ค. ์—ฌ๋Ÿฌ๋ถ„์€ ๊ทธ๋Ÿฌ์ง€ ๋งˆ์„ธ์š”.. ์ด ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜๋Š” ์•„์ด๋””์–ด๋Š”๋Œ€์นญ ๊ตฌ์กฐ๋กœ ์นœ๊ตฌ ๊ด€๊ณ„๋ฅผ ์ €์žฅํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ์˜ˆ์ œ์™€ ํ•จ๊ป˜ ์•Œ์•„๋ณด์ž. ์˜ˆ์ œ 1์„ ๋ณด๋ฉด ์ฒซ ์ค„์— 3(๋ช…) 2(๊ฐ€์ง€ ๊ด€๊ณ„๋ฅผ ์ž…๋ ฅํ•  ์˜ˆ์ •)์ด ๋‚˜ํƒ€๋‚˜์žˆ๋‹ค.๊ทธ ์•„๋ž˜ 1๊ณผ 2๊ฐ€ ์นœ๊ตฌ, 2์™€ 3์ด ์นœ๊ตฌ์ธ ์ƒํƒœ๋ผ๊ณ  ์•Œ๋ ค์ฃผ๊ณ  ์žˆ๋‹ค. ์ด๋ฅผ 3x3๋ฐฐ์—ด์— ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ €์žฅํ•œ๋‹ค๊ณ  ์ƒ๊ฐํ•ด๋ณด์ž. ์‚ฌ๋žŒ1์‚ฌ๋žŒ2์‚ฌ๋žŒ3์‚ฌ๋žŒ1X(์นœ๊ตฌ์•„๋‹˜)O(์นœ๊ตฌ์ž„)X์‚ฌ๋žŒ2OXO์‚ฌ๋žŒ3XOX 1๊ณผ 3์ด ์นœ๊ตฌ๋ฉด 3๊ณผ 1๋„ ๋‹น์—ฐํžˆ ์นœ๊ตฌ์ธ ๊ฒƒ์ด๋‹ˆ ์œ„์™€ ๊ฐ™์ด ๋Œ€๊ฐ์„  ๋Œ€์นญ์ธ ํ‘œ๋ฅผ ๊ทธ๋ฆด ์ˆ˜ ์žˆ๋‹ค. ์ด ์›๋ฆฌ๋ฅผ ์‚ฌ์šฉํ•ด ๋‹ค์Œ๊ณผ ๊ฐ™์ด ์ฝ”๋“œ๋ฅผ ์ž‘์„ฑํ–ˆ๋‹ค.#include #.. 2023. 8. 4.
[JOV-2007] Measuring visual clutter(๋…ผ๋ฌธ๋ฆฌ๋ทฐ) ์˜ค๋Š˜ ์š”์•ฝํ•ด๋ณผ ๋…ผ๋ฌธ์€ ์ตœ๊ทผ ์ฝ์€ Ruth Rosenholtz, Yuanzhen Li, Lisa Nakano, "Measuring visual clutter", Journal of Vision August 2007, Vol.7, 17. ์ž…๋‹ˆ๋‹ค. [JOV-2007]Measuring visual clutter https://doi.org/10.1167/7.2.17 Measuring visual clutter | JOV | ARVO Journals We again looked at RTs for correct trials, separating target-present from target-absent trials. For each image, we averaged RT over Gabor type, targe.. 2023. 8. 2.