情報・システムソサイエティ
Contents
1. 3 20 9 00 12 00 B 2 202 oi O ROR AEE SRS NEC Fw 3 FT s RE EI RE EE ER RE ER RE RE ES2. 10 45 0 gt 8 PP v Si BD 3 20 13 30 16 15
3. oe Bag oEColors err OH 15 00 NTT Convolutional Neural Networks foolog ee 188 mh3SBR8RWYESRH2HERSSSESRSRRHH 1 Kinect ooo Chaiyapruk Thongkam
4. 78 D 17 3 21 10 00 12 00 B 1 107 D 17 1 PP PCKCTOYY KTX NTT D 17 2 Pe NTT D 17 3 GPA D 17 4 Android 11 15 D 175 ee TO APS D 176 ee NEC
5. D 12 75 D 12 76 ES STR Nd NRE STREET EE D 12 77 reer D 12 78 KDDI Tsuhan Chen Cornell Univ D 12 79 er 15 00 D 12 80 RISER ERR RRR REE RR RTE RE ERR ERE gt s s D 12 81
6. D 20 3 SSSR SATE thst 11 00 D 20 4 E D 20 5 ee D 20 6 D 20 7 MapReduce RR ESR RNR ERS RAR RN RRR ERR ER ER RE D 21 3 18 13 00 15 30 B 1 108 D 21 1
7. 2 ee EET GE 3 19 13 00 17 00 B 2 204 NTT 3 ee 3 er
8. D 10 3 8 W ee D 10 4 sins tap D 10 5 iat D 10 6 IC ee D 10 7 RAM D 10 8 D 11 3 18 9 15 12 00 B 2 204 KDDI D 11 1
9. D 7 3 ERR ER RR RE D 74 ooo D 7 5 Std ESSN STIL D 7 6 14 45 D 7 7 D 7 8 SR RR EE ERR RR ERE RR EE RS D 7 9 HH
10. 7 DS 1 8 Taufiqurrachman 14 35 DS 1 9 GPU ee NID DS 1 10 2 DS 1 11 Polar 9 818 81975 G97915 9 Ski 81 81808 36Si57 O DS 2 3 20 9 00 11 45 B 2 258 DS 2 1 50 DTN DS 2 2 NER RN EE RRR ERNIE EI ETRE REN s
11. HEVC RRA RNR RT ER ERR O KDDI Mapping HEVC Motion Estimation In GPU 0 ii OFan Wang Dajiang Zhou Satoshi Goto Waseda Univ HEVC ee HEVC QO 14 45 Memory Organization in HEVC Motion Compensation For UHDTV Luma and Chroma Parallel eeseeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeeseeeeeeeeeeeeee OShihao Wang lt Dajiang Zhou Satoshi Goto Waseda Univ Proposed Bypass algorithm of HEVC CABAC Decoder kk OLangping He Waseda Univ Kaoru Matsuoka Toshiba Satoshi Goto Waseda Univ A Highly Efficient VLSI Architecture For HEVC CABAC Decoder REESE RR RRS RA ENE OYijin Zhao JinJia Zhou Satoshi Goto Waseda Univ An Efficient Hardware Architecture For Intra Prediction Module in HEVC OJianbin Zhou Dajiang Zhou Satoshi Goto Waseda Univ HEVC
12. D 19 3 API SN D 19 4 HTTP Web ee 10 45 D 19 5 AES D 196 Web emer D 19 7 IC omo D 19 8 err D 20 3 20 10 00 12 00 B 2 207 D 20 1 D 20 2
13. 83 8 18HS 8 O D 11 2 HDR D 11 3 D 11 4 re D 11 5 D 11 6
14. D 15 3 3D PPPCTPPPPPPPPPPPPPPFFPPPPPPPPPPPPPPPPPCYKTPPPFTYYTYYT KDDI 77 D 15 4 e Testing AKA D 15 5 14 30 D 15 6 PDF D 157 ee
15. D 7 10 D 7 11 a ANS D 7 12 Fed D 8 3 19 13 00 14 45 B 3 354 D 8 1 2S1G08 ie 53805058 58895 15 D 8 2 ee D 8 3 EAX GPU
16. 3D SERS ERED ES Matching Cost Depth Map ee O KDDI Kinect Fusion 3 Hess aS Bs EST ERY KDDI SRE RRND TER Nrs ts 63891I9 SI 15 15 ee
17. LMP ee Blinn Phong ii NEC gi878 9580818i9 89766 90899218 Si 019 29 THEOS 3 3 20 9 00 12 00 B 2 204 0 3
18. D 11 7 ee D 11 8 MRI ee D 11 9 it D 11 10 eat D 11 11 3 18 13 00 17 00 B 2 204 D 11 12 2 OFDM D 11 13 ee eee
19. Critical View RRR D 16 8 MR oo 15 30 D 16 9 200kHz KTN SS NS gs2asidssa NTT D 16 10 3 MRI D 16 11 MRI SNR iS D 16 12 Q oon
20. 3D Kinect 3 00878Y88te238 6198Y 58797 NII 3 3 19 13 00 16 45 B 2 202 KDDI Midq Level Patch Fine Grained Mei NTT
21. D 21 2 STSS D 21 3 ID D 21 4 SNS 62038 18 533885830 3 1878 8866818 51678869IS8 8 8269854198087si9 8 gt s D 21 5 eee 14 30 D 21 6 NR te NTT D 21 7 3D ememrnrnrnrnrnin D 21 8 RE RR RA EN RR NINE
22. A Sg NTT AT AR ALOS RA EE 11 00 ee NTT NTT 920939 3 18
23. D 15 15 ee D 15 16 RG EE D 15 17 3 0 D 15 18 RR RN D 15 19 SE a NR i et Da Tmo 3 21 13 00 15 30 F 3 372 D 15 20 SR
24. FFP GT FPP D 4 18 ee Hoan Tran Quoc D 4 19 89838 fBSEH33SBSSE3SSEE SS389ERES3SRBRGES3GE T D 4 20 ERE RE EAE D 5 3 18 15 00 16 15 B 2 206 D 5 1 D 5 2 D 5 3
25. O D 4 11 Focused Crawler D 4 12 Web oo D 413 D 4 14 14 30 D 4 15 D 4 16 Jianwei Zhang D 417
26. D 11 14 HCL D 11 15 ee D 11 16 SD D 11 17 ee D 11 18 ee D 11 19 ERR RRR RRR ER RR ER RE RA ES TEER a 15 15 KDDI D 11 20
27. D 21 9 ee 79 D 23 3 18 9 30 10 45 B 2 258 D 23 1 Memcached SAEED EE ER D 23 2 RR SERENE TRE TIT A NE RE ERE ESR D 23 3 0 ASTEM D 23 4 08203 5 6 186 O s D 23 5
28. ee D 16 3 20 13 00 16 30 B 2 207 D 16 1 D 16 2 ee D 16 3 ERS D 16 4 Random Forest ememnnin 14 15 D 16 5 Study of Recognizing Surgeon s Action during Suture Surgery by Using Sectioned SIET Ye Li Jun Ohya Waseda Univ Toshio Chiba NCCHD Rong Xu Waseda Univ Hiromasa Yamashita NCCHD D 16 6 3 ENR Rn RE eb ER D 16 7 VR
29. 11 00 88182 8181997 2 98 80e5 0 89 19287 882SIS16 78 B519i9 81 1819s 23 SA Local Binary Pattern D 12 27 D 12 28 75 ee LED oo D 12 12 49 12 50 12 29 12 30 12 31 12 32 2 33 2 34 2 35 2 36 2 37 2 38 12 39 12 40 12 51 12 52 12 53 12 54 12 55 12 56 12 57 12 58 12 59 12 60 12 61 12 62 12 63 12 64 12 65 12 66 B
30. D 11 21 MS dat PR SITs NEC NEC NEC 3 3 11 22 11 23 11 24 11 26 1 27 1 28 1 29 1 30 1 31 1 32 1 33 1 34 1 35 1 36 11 37 11 38 11 39 11 40 11 41 11 42 11 43 1 44 1 45 1 46 1 47 1 48 1 49 1 50 1 51 1 52 1 53 1 54 1 55 1 56 1 57 1 58 1 59 11 60 11 61 11 62 73 4K HDTV io NRA SN lt
31. 2 S 3 3 8 0831808 2 187e NTT 3 19 13 00 16 15 B 2 254 D 9 24 4 D 9 25 NTT D 9 26 ee NTT D 9 27 tt D 9 28 ee D 9 29 NNSA ERE RE RRR RR
32. D 15 8 D 15 9 EPUB ee 3 21 9 15 12 00 F 3 372 D 15 10 1ICT PBL 1 RR DE ts D 15 11 ICT PBL 2 SN Rk D 15 12 1ICT PBL 3 SHA 256 ee D 15 13 1ICT PBL 10 30 D 15 14
33. OR 8 si gt 3 1 63 1 64 1 65 1 66 1 67 1 68 1 69 11 70 3 20 13 00 16 30 B 2 204 NTT HEVC NTT SAO
34. RGB L a b R YR 7 RR RES RR RE RR RR EE ER RRR 9 D 3 3 18 9 45 12 00 B 2 206 D 3 1 Ambient Calculus ee D 3 2 ER D 3 3 mee D 3 4 VDM SL 11 00 D 3 5 D 3 6 D
35. RRR ER RR REN EER RNR ERR ER RRR D 12 82 ee D 12 83 QR AR NTT D 12 84 ee D 12 85 ee O D 13 3 20 11 00 12 00 B 1 107 D 13 1 RoboCupRescue P D 13 2 RDF meeeeeeeeeeerreerereerreerrerrerre D 13 3 Web SATIS R
36. DRIER EEA RR RR ER ER RR s 15 00 D 12 69 gt NTT D 12 70 D 12 71 EIR EE EN EEE OE EEE ENE EEE EE RI EEE D 12 72 LIDAR RS D 12 73 O s 3 21 13 15 16 30 B 2 202 D 12 74
37. DS 2 10 SASS Ras O
38. NTT D 6 14 Linux Cgroups CPU throttling D 6 15 MapReduce CUDA D 6 16 D 6 17 CPU RAED ESTEE ER OE D 7 ME A 3 21 13 00 13 30 B 1 103 D 7 1 D 7 2 ee 71 D ME B 3 21 13 30 16 15 B 1 103
39. ee D 14 8 15 30 D 149 hi 4 10 SRR TR REE ERR ER RE REESE RE TE s D 14 11 NIRS Suber hse ra se KAS dQ gt D 14 12 RR RR ERR EE RE ER RA RR ERIE EEE ER ES D 15 3 20 13 00 15 30 F 3 372 D 15 1 D 15 2 ESE SRR O
40. NTT D 5 4 SPs D 5 5 Twitter Et D 6 A B 3 18 13 00 15 15 B 3 356 D 6 1 RAM 0 D 6 2 D 6 3 PCI Express I O NNN D 64 D 65 OS ee D 6 6
41. 14 45 D 9 30 D 9 31 Phy NEC D 9 32 ete D 9 33 RE s D 9 34 D 9 35 CUL D 10 3 20 13 00 15 00 F 3 374 D 10 1 D 10 2
42. ee D 94 ee D 9 5 ee D 9 6 14 45 D 9 7 D 9 8 CT D 9 9 ee NTT D 9 10
43. O N EC Kayali Abudl American Univ of Beirut Kimberly Mcguire Delft Univ of Tech NEC EAAUHHUEDACG Rene M A Teixeira 3 19 9 30 12 00 B 2 204 3 ete ee
44. 10 30 DS 2 3 ooo DS 24 I 7 DS 2 5 81909 15180 3 20 13 00 15 45 B 2 258 DS 2 6 50 de DS 27 DTN Ge 14 30 DS 2 8 DS 2 9 Bluetooth ee
45. ee lt gt DS 1 COMP ELC 3 19 9 05 11 30 B 2 258 DS 1 1 ei NII DS 1 2 ee 10 15 DS 1 3 eee DS 1 4 ee DS 1 5 RNR RR REE EE RR s 3 19 13 00 15 50 B 2 258 DS 16 et DS 1 7
46. D 17 7 er ns APS D 18 3 20 13 00 13 45 B 1 107 D 18 1 LBlock FPGA O gt s D 18 2 3 FPGA s D 18 3 FPGA RIRSRS D 19 3 19 9 30 11 45 B 2 201 D 9 1 CCN Interest 6 179re753878S 6 9615 91805087 WiB2S93828 ai92908918 3891 D 19 2 P2P ee SHRED SNORT ess
47. NTT D 9 18 D 9 19 NTT D 9 20 ee D 9 21 SNS DI RAE RR ERR EERIE RR D 922 72 D 9 23
48. 3 18 9 15 12 00 B 2 201 Structure from Motion ig Q 3 KDDI 10 45 3D ee
49. D 15 21 D 15 22 2 D 15 23 AR SG D 15 24 ee 14 30 D 15 25 e D 15 26 e MT ee D 15 27 D 15 28
50. D 8 ee G 16 00 D 1 9 ee D 1 10 DEVELOPMENT OF ALGORITHM FOR BURST DETECTION OF BETWEENNESS CENTRALITY IN GRAPH STREAM DATA Waranya Mahanan Mori Kazuo Mie Univ Natwichai Juggapong Chiang Mai Univ D 1 11 3 panelLhinge RN NE EI D 1 12 PaneLHinge RE D 2 3 19 9 00 9 45 B 3 350 D21 Concurrent Q Learning D 2 2 ee D 2 3
51. rT D 84 ee D 8 5 D 8 6 DERE RR EI ER ERR ERR RR RRR D 8 7 WR SERRE RE ERRNO EE RN EE EI RE O x D 9 3 18 13 00 16 15 B 2 254 D 9 1 LogView D 92 SNS NTT D 9 3
52. DCT O ERI ARR ei 809 8899 789081859 8 65 98821 g16 Na 4 ee ie 10 45 NTT PPPCCTPPPPPPPPPPPPPFFPPPPPPFPPPPPPPPPPPPCPPPPPPYKYYTYYYPTYYPYYPYP FI NHK RGB DOCDCCPPCPPPPPPPPPPPPPPPPPTPPPPPPPCLPPCUPCCPPPLYTYYPPLPLYYPYT
53. 11 00 D 46 D 4 7 D 4 8 Efficient Reverse kNN Query Algorithm on Road Network Distances Using Partitioned Subgraphs 18018783 0519 382 89028 28188 6 89196 Ge SLBiSiv ONilar Win Tin Yutaka Ohsawa Saitama Univ G 70 D 4 9 OE 3 18 13 00 16 00 F 3 372 D 4 10 WordNet
54. D 9 11 ee OO D 9 12 BPMN ee NTT 3 19 9 00 12 00 B 2 254 NTT D 9 13 I 1 RRR RRR VARNES ERNE D 9 14 UI 2 VR SR RR IER RT RR RRR IRENE RR RRR EE s D 9 15 UI 3 ERE RIE EEE EE NNN ET RENE ENR s D 9 16 3 D 9 17 3 ee 10 30
55. B 2 202 Multiple Object Identification by Clustering Keypoint Correspondences based on Parameters of Similarity Transformaton ORuihan Bao Kyota Higa Kota Iwamoto NEC Supervoxel RN NR Davis Larry Univ of Maryland RA RR 76 D 12 67 eeeeeeeeeereeeeeeeeeereeereereeee D 12 68
56. hae nts tet 8K HEVC RR NS NHK H 265 HEVC No Reference RRR ER REESE EERE RENE ES lt lt KDDI 12 10 12 11 12 12 12 13 12 14 12 15 12 16 12 17 12 18 12 19 12 20 12 21 12 22 12 23 12 24 12 25 12 26 A 3 18 9 30 12 00 B 2 202 ee
57. 13 30 16 15 B 2 202 NTT Deep Learning RS Local Binary Pattern ORR RRR ee Ms 15 00 eee RRO ER OR PR RR RE E
58. 3 7 TavaScript ror O D 3 8 ee D 4 3 18 9 30 12 00 F 3 372 D 4 1 D 4 2 era Para NTT D 4 3 NTT D 4 4 OTT NTT D 4 5 ere
59. Sires tdnet Renna dean shee D 6 7 ee D 6 8 SHA 3 D 6 9 HPC eee D 6 C 3 18 9 30 11 30 B 3 356 D 6 10 0O R ee D 6 11 OS 0 D 6 12 Catalogue OS D 6 13 KVM CPU
60. 69 gt D 1 3 18 13 30 17 00 B 2 258 D 1 1 D 12 ET D 1 3 2 D 1 4 14 45 D 1 5 er D 1 6 P T a RRR D 1 7
61. RR PE Kinect Liang Li Kinect WX ho 3 19 9 30 12 00 B 2 202 RE SIE EE EEE ERR RE EER Sn i 1 O EER ER SE ER ER EE SEEN EIS RE RR ERNE OE
62. T TPE T ERRRT D 13 4 NTT D 14 3 19 13 00 16 30 B 2 207 D 14_1 HMM D 142 Tulius D 143 D 14 4 14 15 D 14 5 ee D 146 DirectShow D 14 7
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