1,720,959 research outputs found
DISTRIBUTED SYSTEM MODEL FOR KEY MANAGEMENT
Get PDFKey management plays a crucial role in cryptography, as the basis for secure information exchange, data identification and integrity. There are software and hardware key management tools that support Crypto APIs and Cryptography Next Generation APIs(CNG API), Public Key Cryptography Standards (PKCS). These tools store cryptographic keys on hard disks, smart cards, tokens, and in other storage media. To use the cryptographic keys stored on these smart cards and tokens, you need to connect them to the appropriate hardware. The cryptographic keys stored on the hard drives of a computer or a laptop are used by the programs of these devices. If it becomes necessary to use a single key in different systems, then you will have to create copies of the key on all these devices. This complicates the process of key management, raises tasks of securely store keys, keys access control. This paper proposes a distributed system model for key management and a protocol of interaction of the distributed system modules. The proposed model provides the ability to store keys in a smartphone, and access to keys from other devices. The system described in the model consists of 3 modules. The module 1 has computer version and smartphone version, and serves to send a request for signing, signature verification, hashing. The module 2, a smartphone software, provides key pair generation, storing, encrypting and decrypting, archiving keys, export/import keys, keys access control, and destroying keys. The module 3, web service, provides communication of the first and second modules. In addition, the system, which was created based on the current model, provides the ability to use digital signatures in web applications. The Module 1 operates as a local web service that accepts requests from a web page running in a browser. A special script in a web page sends http requests that include cryptographic operations to the specified localhost port and accepts responses
MAʼLUMOTLAR TOʻPLAMINI OʻQITISH, BAHOLASH VA TEST TOʻPLAMLARIGA AJRATISH USULLARI
No full textMashinali oʻqitish algoritmlari maʼlumotlardagi shablon(qolip)larni oʻrganadi va ulardan yangi maʼlumotlar boʻyicha bashorat qilish uchun foydalanadi. Ishlab chiqilgan sun`iy intellekt modellari ish faoliyatini ularning oʻqitish jarayonida koʻrmagan maʼlumotlarga qarab baholash muhim. Bu ishni maʼlumotlarni taqsimlash yoki ajratish usuli orqali hal qilish mumkin. Ushbu maqolada mashinali oʻqitishda maʼlumotlar toʻplamini oʻqitish, baholash va test toʻplamlariga ajratish hamda modelni samaradorlik (accuracy), aniqlik (precision), eslab qolish(recall) va F1-ball (F1 score) orqali baholash usullari keltiriladi
KOMPYUTER TARMOQLARIDA MAXFIY MA’LUMOTLARNI AI ASOSIDAGI ANIQLASH METODLARI
No full textMazkur maqolada, kompyuter tarmoqlarida uzatilayotgan va saqlanayotgan konfidentsial axborotlarni AI (Sun’iy intellekt) metodlari yordamida aniqlash yondashuvlari to‘g‘risida fikr yuritiladi.Hozirgi murakkab va dinamik tarmoq muhitida an’anviy yondashuvlar asosida tarmoqdagi ma’lumotlarni aniqlash yoki sizib chiqishini oldini olish yetarlicha foyda bermayotgani sababli mashinali o‘rganish va chuqur o‘rganish modellari tahlil qilinadi. Tadqiqotda klassifikatsiya va anomal holatlarni aniqlash usullarining ustunligi shuningdek ularni real tarmoq muhitida qo‘llashga doir fikr yuritiladi. Natijalar AI asosidagi metodlar kompyuter tarmoqlarida axborot xavfsizligini oshirishda muhim ahamiyatga ega ekanligini ko‘rsatadi
TABIIY TILNING STATISTIK MODELLARI
No full textTabiiy tilning statistik modeli (Statistical Language Model, SLM) – tabiiy tilni qayta ishlashda qo‘llaniladigan zamonaviy vosita bo‘lib, u ma’lum tildagi so‘zlar ketma-ketligi ehtimolini bashorat qilishga qaratilgan. SLM asosida gapdagi muayyan ketma-ketlikdan keyingi so‘z bashorat qilinadi. SLM so‘zlarning tabiiy til ma’lumotlari korpusida paydo bo‘lishiga asoslangan ketma-ketlik ehtimolini hisobga oladi. Katta hajmdagi matn ma’lumotlarini tahlil qilish orqali model so‘zlarning tilda qanday qo‘llanilishi qoliplarini o‘rganishi va ushbu qoliplar asosida keyingi ehtimoli yuqori so‘zni bashorat qilishi mumkin. NLP sohasi rivojlanishda davom etar ekan, statistik til modellari tilni tushunish va qayta ishlash uchun asosiy vosita bo‘lib hisoblanadi. SLMlar yordamida tabiiy til texnologiyasida mumkin bo‘lgan chegaralarni kengaytirishni davom ettirishimiz va yanada innovatsion va kuchli NLP ilovalarni yaratishimiz mumkin. Ushbu maqolada tabiiy tilning statistik modellaridan hiosblangan N-gram modelini o‘zbek tili korpusi asosida ishlab chiqish usullari keltiriladi. Shuningdek, N-gram modellarining matematik tavsifi va baholash usullari hamda umumlashtirish, sezgirlik, OOV (noma’lum so‘zlar), maxsus kontekst muammolari va ularni bartaraf qilish yo‘llari keltiriladi
MATNLARNI RAQAMLASHTIRISH VA MASHINALI O‘QITISHDA WORD2VEC METODINING AHAMIYATI
No full textTabiiy tilni qayta ishlash (NLP) ‒ tilshunoslik, kompyuter fanlari, sun’iy intellektning kompyuter va insonning o‘zaro ta’siri bilan bog‘liq bo‘lgan bo‘limi. U asosan, tabiiy tilni qayta ishlash va baholash uchun metod, algoritm va axborot tizimlarini loyihalash, ishlab chiqish masalalari bilan shug‘ullanadi. Hozirda NLP usullari vositasida katta hajmdagi til korpuslari va millionlab veb-sahifalar bir soniya ichida tahlil qilinadi. Shuningdek, NLP vazifalarini yechishda statistik va neyron tarmoqli metodlar qo‘l kelmoqda. Ko‘pgina NLP ilovalari chuqur neyron tarmoq usullaridan foydalanib, texnologik taraqqiyot, kompyuter quvvatining ortishi va katta hajmdagi til korpuslarining mavjudligi tufayli samarali ishlamoqda. Matnli ma’lumotlarining aksariyati strukturlanmagan, Internetda mavjud yoki turli manbalarda joylashgan. Matnli ma’lumotlar to‘g‘ri olingan, jamlangan, formatlangan va tahlil qilingan bo‘lsa, ahamiyatli va foydali bo‘la oladi. Matn tahlilini to‘g‘ri amalga oshirish kompaniya va tashkilotlarga turli yo‘llar bilan foyda keltirishi mumkin. Strukturalanmagan matnni tahlil qilish usullari matn tasnifi, hissiyotlarni tahlil qilish, NER obyektlarni aniqlash va mavzuni modellashtirish kabi vazifalarini qamrab oladi. NLPning ushbu vazifalari turli kontekstlarda qo‘llaniladi. Ularni bajarish uchun, birinchi navbatda, mashina inson tilini tushunishi, qayta ishlashi uchun nutq va matnlarni raqamli shaklga o‘tkazish zarur. Tabiiy tilni talqin qiluvchi, tushunuvchi aqlli tizimlarni ishlab chiqishda strukturlanmagan matnli ma’lumotlar bilan ishlash, ularni sun’iy intellekt metodlari vositasida qayta ishlash maqsadida raqamli shaklga o‘tkazish lozim. So‘zlarni joylashtirish – bu tabiiy tildagi leksik birliklarning umumiy semantikasi va lingvistik shablonlarini qamrab oluvchi so‘zlarning muayyan (fiksirlangan) uzunlikdagi vektor ko‘rinishlari. NLP tadqiqotchilari bunday tasvirlarni olishning turli usullarini taklif qilishgan. Jumladan, Word2ec 2013-yilda Google kompyaniyasi tadqiqotchilari tomonidan ishlab chiqilgan matnni qayta ishlashga va raqamlashtirishga mo‘ljallangan metod bo‘lib, uning asosiy maqsadi so‘zlarni vektorlar orqali ifodalashdan iborat. Word2vec metodi vostasida matndagi so‘zlarning semantikasi ma’no jihatdan kodlanadi. Ushbu maqolada Python tilidagi NumPy paketidan foydalangan holda word2vec metodi orqali o‘zbek tili matnlaridagi so‘zlarni raqamlashtirishni amalda qo‘llash masalasi tahlil qilinadi
Going Beyond Counting First Authors in Author Co-citation Analysis
Get PDFThe present study examines one of the fundamental aspects of author co-citation analysis (ACA) - the way co-citation
counts are defined. Co-citation counting provides the data on which all subsequent statistical analyses and mappings
are based, and we compare ACA results based on two different types of co-citation counting - the traditional type that
only counts the first one among a cited work's authors on the one hand and a non-traditional type that takes into
account the first 5 authors of a cited work on the other hand. Results indicate that the picture produced through this non-traditional author co-citation counting contains more coherent author groups and is therefore considerably clearer. However, this picture represents fewer specialties in the research field being studied than that produced through the traditional first-author co-citation counting when the same number of top-ranked authors is selected and analyzed. Reasons for these effects are discussed
Variations on the Author
Get PDF“Variations on the Author” discusses two of Eduardo Coutinho’s recent films (Um Dia na Vida, from 2010, and Últimas Conversas, posthumously released in 2015) and their contribution to the general question of documentary authorship. The director’s filmography is characterized by a consistent yet self-effacing form of authorial self-inscription: Coutinho often features as an interviewer that rather than express opinions propels discourses; an interviewer that is good at listening. This mode of self-inscription characterizes him as an author who is not expressive but who is nonetheless markedly present on the screen. In Um Dia na Vida, however, Coutinho is completely absent form the image, while Últimas Conversas, on the contrary, includes a confessional prologue that moves the director from the margins to the center of his films. This article examines the ways in which these works stand out in the filmography of a director who offers new insights into the notion of cinematic authorship
Appropriate Similarity Measures for Author Cocitation Analysis
Get PDFWe provide a number of new insights into the methodological discussion about author cocitation analysis. We first argue that the use of the Pearson correlation for measuring the similarity between authors’ cocitation profiles is not very satisfactory. We then discuss what kind of similarity measures may be used as an alternative to the Pearson correlation. We consider three similarity measures in particular. One is the well-known cosine. The other two similarity measures have not been used before in the bibliometric literature. Finally, we show by means of an example that our findings have a high practical relevance.information science;Pearson correlation;cosine;similarity measure;author cocitation analysis
Dispelling the Myths Behind First-author Citation Counts
Get PDFWe conducted a full-scale evaluative citation analysis study of scholars in the XML research field to explore just how different from each other author rankings resulting from different citation counting methods actually are, and to demonstrate the capability of emerging data and tools on the Web in supporting more realistic citation counting methods. Our results contest some common arguments for the continued
use of first-author citation counts in the evaluation of scholars, such as high correlations between author rankings by first-author citation counts and other citation
counting methods, and high costs of using more realistic citation counting methods that are not well-supported by the ISI databases. It is argued that increasingly available digital full text research papers make it possible for citation analysis studies to go beyond what the ISI databases have directly supported and to employ more
sophisticated methods
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