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Articoli di riviste sul tema "BIOMETRICAL"

Autore: Grafiati

Pubblicato: 11 settembre 2023

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1

Babenko, L. K., O. B. Makarevitch e E. P. Tumoian. "BIOMETRICAL CRYPTOSYSTEMS. A WAY TO SECURED BIOMETRICS". Vestnik Yuzhnogo nauchnogo tsentra 1, n. 3 (2005): 95–99. http://dx.doi.org/10.23885/1813-4289-2005-1-3-95-99.

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2

Žagar, Dejan, Matija Svetina, Tanja Brcko, Marko Perkovič, Franc Dimc e Andrej Košir. "Analysis of Marine-Pilot Biometric Data Recordings during Port-Approach Using a Full-Mission Simulator". Sensors 22, n. 7 (31 marzo 2022): 2701. http://dx.doi.org/10.3390/s22072701.

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Abstract (sommario):

The purpose of this study is to analyse data from the marine pilots’ bio-sensor readings to determine how experience affects their biometrical response during the port approach. The experiences play a significant role in the participant’s decision-making process and correlate with the repetitions. Through the repetitions of the experimental task, the participants gain experience, which correlates with the biometrical response, e.g., heart rate, electrodermal activity, etc. After exposing the two experience-distinct groups of participants to the same simulated port-approaching task, their collected biometric data is analysed and discussed. The results show that biometrical readings of the less experienced participants typically vary compared to that of the experienced participants, who take the simulated task more seriously. The study also yields insight into the workload process, involving disturbing factors during the task.

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3

Evans, David M., N. A. Gillespie e N. G. Martin. "Biometrical genetics". Biological Psychology 61, n. 1-2 (ottobre 2002): 33–51. http://dx.doi.org/10.1016/s0301-0511(02)00051-0.

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4

Sheranova, Arzuu. "Cheating the Machine: E-voting Practices in Kyrgyzstan’s Local Elections". European Review 28, n. 5 (26 marzo 2020): 793–809. http://dx.doi.org/10.1017/s1062798720000241.

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This paper analyses the e-voting experience of the local elections undertaken by Osh city Council in 2016. The process was introduced to ensure fair and democratic elections in Kyrgyzstan after continuous and repeated violent political uprising. The e-system, based on biometrics registration, biometric identification of voters and automated vote counting, was designed to help to avoid the most common election frauds: vote buying, carousel voting and group/family voting. The case study, mainly based on interviews, illustrates the adaptation and modernization of strategies to resist and cheat within the e-voting system. The analysis outlines three widely practised cheating strategies: procedural violations, such as avoiding cross-checking of manual and automated counting and allowing voting without biometrical identification; transformation of bribery into ‘vote auctioning’; and strengthening of kinship-based/regional support and tribal/regional identity under conditions of e-voting.

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5

Salman, Duha D., Raghad A. Azeez e Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm". Engineering and Technology Journal 38, n. 3B (25 dicembre 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.

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6

Salman, Duha D., Raghad A. Azeez e Adul mohssen J. Hossen. "Key Generation from Multibiometric System Using Meerkat Algorithm". Engineering and Technology Journal 38, n. 3B (25 dicembre 2020): 115–27. http://dx.doi.org/10.30684/etj.v38i3b.652.

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Abstract (sommario):

Biometrics are short of revocability and privacy while cryptography cannot adjust the user’s identity. By obtaining cryptographic keys using biometrics, one can obtain the features such as revocability, assurance about user’s identity, and privacy. Multi-biometrical based cryptographic key generation approach has been proposed, subsequently, left and right eye and ear of a person are uncorrelated from one to other, and they are treated as two independent biometrics and combine them in our system. None-the-less, the encryption keys are produced with the use of an approach of swarm intelligence. Emergent collective intelligence in groups of simple autonomous agents is collectively termed as a swarm intelligence. The Meerkat Clan Key Generation Algorithm (MCKGA) is a method for the generation of a key stream for the encryption of the plaintext. This method will reduce and distribute the number of keys. Testing of system, it was found that the keys produced by the characteristics of the eye are better than the keys produced by the characteristics of the ear. The advantages of our approach comprise generation of strong and unique keys from users’ biometric data using MCKGA and it is faster and accurate in terms of key generation.

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7

Chmielińska, Jolanta, e Jacek Jakubowski. "Biometrical driver face verification". AUTOBUSY – Technika, Eksploatacja, Systemy Transportowe 19, n. 6 (7 settembre 2018): 68–72. http://dx.doi.org/10.24136/atest.2018.039.

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The paper discusses the problem of face verification in a driver monitoring system for the purpose of traffic safety. Two different methods of face verification were proposed. Both of them are based on a convolutional neural network and were developed with the use of a transfer learning technique. In the paper, the results produced by both proposed method have been presented and compared. Moreover, their advantages and disadvantages have been discussed.

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8

Ruttner, Friedrich. "Biometrical Control of Breeding". Journal of Apicultural Research 30, n. 2 (gennaio 1991): 113–14. http://dx.doi.org/10.1080/00218839.1991.11101243.

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9

Berghold, Andrea, Hans Ulrich Burger, Ludwig A. Hothorn e Andreas Ziegler. "50 Years Biometrical Journal". Biometrical Journal 50, n. 1 (febbraio 2008): 5–7. http://dx.doi.org/10.1002/bimj.200710394.

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10

Lodge, Juliet, e Dan Nagel. "Interview with Isabelle Moeller: Ubiquitous and Positive Biometrics". International Review of Information Ethics 27 (1 dicembre 2018). http://dx.doi.org/10.29173/irie102.

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Abstract (sommario):

In this interview with Isabelle Moeller questions surrounding the responsible use and development of biometric apps are explored. The use of biometrics - originally to digitally represent and authenticate an identifiable characteristic of a product or a person - have become so wide spread that they are capable of facilitating ever greater continuous surveillance of the what, how, when and where of life. The more biometrical data of a user is collected, the more the integrity of the underlying e-identity is open to fraud and being invisibly compromised. Ethical reflection is long overdue but a prerequisite of minimizing risk to the autonomy of the human person as well as to the integrity of his digital persona.

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11

"Contents: Biometrical Journal". Biometrical Journal 51, n. 6 (dicembre 2009): 883–84. http://dx.doi.org/10.1002/bimj.200990002.

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12

"Contents: Biometrical Journal". Biometrical Journal 52, n. 2 (febbraio 2010): 167–68. http://dx.doi.org/10.1002/bimj.201090001.

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13

"Contents: Biometrical Journal". Biometrical Journal 52, n. 3 (giugno 2010): 295–96. http://dx.doi.org/10.1002/bimj.201090002.

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14

"Contents: Biometrical Journal". Biometrical Journal 52, n. 4 (agosto 2010): 447–48. http://dx.doi.org/10.1002/bimj.201090004.

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15

"Contents: Biometrical Journal". Biometrical Journal 52, n. 5 (ottobre 2010): 575. http://dx.doi.org/10.1002/bimj.201090006.

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16

"Contents: Biometrical Journal". Biometrical Journal 52, n. 6 (dicembre 2010): 703–4. http://dx.doi.org/10.1002/bimj.201090008.

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17

"Contents: Biometrical Journal". Biometrical Journal 54, n. 2 (marzo 2012): 161–62. http://dx.doi.org/10.1002/bimj.201170003.

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18

"Contents: Biometrical Journal". Biometrical Journal 53, n. 1 (24 gennaio 2011): 3–4. http://dx.doi.org/10.1002/bimj.201190001.

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19

"Contents: Biometrical Journal". Biometrical Journal 53, n. 2 (marzo 2011): 165–66. http://dx.doi.org/10.1002/bimj.201190003.

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20

"Contents: Biometrical Journal". Biometrical Journal 53, n. 3 (maggio 2011): 391–92. http://dx.doi.org/10.1002/bimj.201190006.

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21

"Contents: Biometrical Journal". Biometrical Journal 53, n. 4 (30 giugno 2011): 541–42. http://dx.doi.org/10.1002/bimj.201190009.

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22

"Contents: Biometrical Journal". Biometrical Journal 53, n. 5 (settembre 2011): 713–14. http://dx.doi.org/10.1002/bimj.201190012.

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23

"Contents: Biometrical Journal". Biometrical Journal 53, n. 6 (novembre 2011): 871–72. http://dx.doi.org/10.1002/bimj.201190015.

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24

"Masthead: Biometrical Journal". Biometrical Journal 54, n. 1 (gennaio 2012): 2. http://dx.doi.org/10.1002/bimj.201290001.

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25

"Contents: Biometrical Journal". Biometrical Journal 54, n. 1 (gennaio 2012): 3–4. http://dx.doi.org/10.1002/bimj.201290002.

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26

Tumuli, Andika D. L., Xaverius B. N. Najoan e Alwin Sambul. "Implementasi Teknologi Biometrical Identification untuk Login Hotspot". Jurnal Teknik Informatika 12, n. 1 (26 ottobre 2017). http://dx.doi.org/10.35793/jti.12.1.2017.17873.

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Abstract (sommario):

Seiring dengan berkembangnya teknologi dewasa ini, perusahaan-perusahaan yang sebelumnya melakukan absensi kehadiran secara manual atau masih menggunakan absen tertulis mulai digantikan secara elektronik. Salah satu metode absensi yang paling ampuh dan banyak digunakan adalah absensi dengan menggunakan sidik jari. Hal ini karena identifikasi jenis ini terbukti aman, nyaman dan ekonomis.Sidik jari tidak hanya dapat digunakan sebagai absensi yang terjamin keamanan dan kenyamanannya, sidik jari juga mulai dimanfaatkan sebagai alat login dalam suatu aplikasi. Penelitian dengan judul “Implementasi Teknologi Biometrical Idenfication untuk Login Hotspot” ini mengembangkan sebuah metode untuk memanfaatkan sidik jari sebagai sarana dalam mengotentifikasi seseorang ketika melakukan aktivitas browsing atau menggunakan internet dengan wifi di tempat-tempat umum.Implementasi atau penerapan teknologi biometrical identification bisa menjadi solusi untuk menjamin setiap orang yang ingin mengakses internet dengan aman, proses otentifikasi password wifi dengan cepat dan aman. Sistem yang diajukan peneliti adalah user yang sudah terdaftar sidik jarinya, meletakkan jari pada alat pemindai sidik jari dan secara otomatis akan muncul kode untuk login hotspot. Dengan sistem seperti ini user tidak perlu menunggu admin atau seseorang yang mengetahui password login hotspot untuk menanyakan password login hotspot lagi dan juga setiap user memiliki password tersendiri.Kata kunci — hotspot, sidik jari, biometric.

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27

"Contents: Biometrical Journal 1'21". Biometrical Journal 63, n. 1 (gennaio 2021): 5–6. http://dx.doi.org/10.1002/bimj.202170014.

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"Masthead: Biometrical Journal 1'21". Biometrical Journal 63, n. 1 (gennaio 2021): 4. http://dx.doi.org/10.1002/bimj.202170013.

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29

"Contents: Biometrical Journal 4'21". Biometrical Journal 63, n. 4 (aprile 2021): 696–97. http://dx.doi.org/10.1002/bimj.202170044.

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30

"Masthead: Biometrical Journal 2'21". Biometrical Journal 63, n. 2 (febbraio 2021): 220. http://dx.doi.org/10.1002/bimj.202170023.

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"Contents: Biometrical Journal 2'21". Biometrical Journal 63, n. 2 (febbraio 2021): 221–22. http://dx.doi.org/10.1002/bimj.202170024.

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"Masthead: Biometrical Journal 4'21". Biometrical Journal 63, n. 4 (aprile 2021): 695. http://dx.doi.org/10.1002/bimj.202170043.

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33

"Contents: Biometrical Journal 6'22". Biometrical Journal 64, n. 6 (agosto 2022): 988. http://dx.doi.org/10.1002/bimj.202270064.

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"Masthead: Biometrical Journal 6'22". Biometrical Journal 64, n. 6 (agosto 2022): 987. http://dx.doi.org/10.1002/bimj.202270063.

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"Contents: Biometrical Journal 7'22". Biometrical Journal 64, n. 7 (ottobre 2022): 1157–58. http://dx.doi.org/10.1002/bimj.202270074.

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"Masthead: Biometrical Journal 7'22". Biometrical Journal 64, n. 7 (ottobre 2022): 1156. http://dx.doi.org/10.1002/bimj.202270073.

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37

"Contents: Biometrical Journal 5'22". Biometrical Journal 64, n. 5 (giugno 2022): 833–34. http://dx.doi.org/10.1002/bimj.202270054.

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38

"Masthead: Biometrical Journal 5'22". Biometrical Journal 64, n. 5 (giugno 2022): 832. http://dx.doi.org/10.1002/bimj.202270053.

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"Masthead: Biometrical Journal 5'21". Biometrical Journal 63, n. 5 (giugno 2021): 912. http://dx.doi.org/10.1002/bimj.202170053.

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"Contents: Biometrical Journal 5'21". Biometrical Journal 63, n. 5 (giugno 2021): 913–14. http://dx.doi.org/10.1002/bimj.202170056.

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41

"Contents: Biometrical Journal 8'21". Biometrical Journal 63, n. 8 (dicembre 2021): 1553–54. http://dx.doi.org/10.1002/bimj.202170084.

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42

"Masthead: Biometrical Journal 8'21". Biometrical Journal 63, n. 8 (dicembre 2021): 1552. http://dx.doi.org/10.1002/bimj.202170083.

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43

"Contents: Biometrical Journal 6'21". Biometrical Journal 63, n. 6 (agosto 2021): 1157–58. http://dx.doi.org/10.1002/bimj.202170064.

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"Masthead: Biometrical Journal 6'21". Biometrical Journal 63, n. 6 (agosto 2021): 1156. http://dx.doi.org/10.1002/bimj.202170063.

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"Masthead: Biometrical Journal 3'21". Biometrical Journal 63, n. 3 (marzo 2021): 467. http://dx.doi.org/10.1002/bimj.202170033.

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"Contents: Biometrical Journal 3'21". Biometrical Journal 63, n. 3 (marzo 2021): 469–70. http://dx.doi.org/10.1002/bimj.202170034.

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47

"Contents: Biometrical Journal 2'22". Biometrical Journal 64, n. 2 (febbraio 2022): 195–96. http://dx.doi.org/10.1002/bimj.202270024.

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"Masthead: Biometrical Journal 2'22". Biometrical Journal 64, n. 2 (febbraio 2022): 194. http://dx.doi.org/10.1002/bimj.202270023.

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"Contents: Biometrical Journal 3'22". Biometrical Journal 64, n. 3 (marzo 2022): 417–18. http://dx.doi.org/10.1002/bimj.202270034.

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"Masthead: Biometrical Journal 3'22". Biometrical Journal 64, n. 3 (marzo 2022): 416. http://dx.doi.org/10.1002/bimj.202270033.

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