J language

Author: a | 2025-04-25

Visual J (pronounced jay-sharp ) is a discontinued implementation of the J programming language that was a transitional language for programmers of Java and Visual J languages

Languages that start with J- List of Languages Names

S, Cuofano M, Desiato D (2022) Enhancing spatial perception through sound: mapping human movements into MIDI. Multimed Tools Appl 81(1):73–94. Google Scholar Pietro B, Di Gregorio M, Romano M, Sebillo M, Vitiello G, Solimando G (2020) Sign language interactive learning-measuring the user engagement. In Learning and Collaboration Technologies. Human and Technology Ecosystems: 7th International Conference, LCT 2020, Denmark: Springer, pp 3–12Yash J, Pooja S, Pradnya P, Jyoti W (2017) Sign language to speech conversion using arduino. Int J Innov Eng Res 2(1):37–44. Souza CR, Pizzolato EB (2013) Sign language recognition with support vector machines and hidden conditional random fields: going from fingerspelling to natural articulated words. In: Machine learning and data mining in pattern recognition: 9th International Conference, MLDM 2013, New York, NY. Proceedings, vol 9. Springer Berlin Heidelberg, pp 84–98Gattupalli S, Ghaderi A, Athitsos V (2016) Evaluation of deep learning based pose estimation for sign language recognition. In ACM International Conference Proceeding Series, Association for Computing Machinery. Kadhim R, Khamees M (2020) A real-time american sign language recognition system using convolutional neural network for real datasets. TEM Journal:937–943. D, Bhatt C, Sapariya K, Patel K, Gil-González AB, Corchado JM (2022) Deepsign: Sign language detection and recognition using deep learning. Electronics (Switzerland), 11(11). A, Halder A (2021) Real-time vernacular sign language recognition using mediapipe and machine learning. Int J Res Publ Rev 2(5). A (2013) Applied deep learning - part 4: convolutional neural networks, Medium. Accessed 16 May 2023Thakur A, Budhathoki P, Upreti S, Shrestha S, Shakya S (2020) Real time sign language recognition and speech generation. J Innov Image Process 2(2):65–76. Google Scholar Bantupalli K, Xie Y (2018) American sign language recognition using deep learning and computer vision. In 2018 IEEE International Conference on Big Data (Big Data), IEEE, pp 4896–4899. V, Radpour D (2017) Using deep convolutional networks for gesture recognition in American sign language. arXiv preprint arXiv:1710.06836Sabeenian RS, Sai Bharathwaj S, Mohamed Aadhil M (2020) Sign language recognition using deep learning and computer vision. J Adv Res Dyn Control Syst 12(5 Special Issue):964–968. Google Scholar Shirbhate RS et al (2020) Sign language recognition using machine learning algorithm. Int Res J Eng Technol. [Online]. Available. Accessed 15 May 2023Nano J (2022) Developer kit. NVIDIA Developer. Accessed 23 May 2023Suzen AA, Duman B, Sen B (2020) Benchmark analysis of jetson TX2, Jetson Nano and raspberry PI using deep-CNN. In 2020 International Congress on Human-Computer Interaction, Optimization and Robotic Applications

Jsoftware - The J programming language

2015-08 in Foreign Language Study Colloquial Yoruba Author: Antonia Yetunde Folarin SchleicherPublisher: RoutledgeISBN: 1135995141Category: Foreign Language StudyPage: 312View: 860 DOWNLOAD NOW » Specially written by an experienced teacher for self-study or class use, this easy to use and up to date course provides a step-by-step approach to written and spoken Yoruba with no prior knowledge of the language required. Colloquial Yoruba is:interactive - with lots of exercises for regular practiceclear - providing concise grammar notespractical - with useful vocabulary and pronunciation guidescomplete - including answer key and reference section. By the end of this course you will be able to communicate confidently and effectively in Yoruba in a broad range of everyday situations. Accompany. 2018-03-20 in Foreign Language Study Kayode J. Fakinlede Beginner's Yoruba with Online Audio Author: Kayode J. FakinledePublisher: ISBN: 9780781813716Category: Foreign Language StudyPage: 0View: 696 DOWNLOAD NOW » Yoruba, one of the national languages of Nigeria, is spoken by more than 30 million people worldwide. This book's 15 lessons, designed with the beginning student in mind, are ideal for both classroom use and self-study. The accompanying audio (available for free download) further complements the lessons. 2005 in Foreign Language Study Kayode J. Fakinlede Beginner's Yoruba Author: Kayode J. FakinledePublisher: Hippocrene BooksISBN: 9780781810692Category: Foreign Language StudyPage: 294View: 899 DOWNLOAD NOW » "Beginner's Yoruba" is now available with two accompanying audio CDs. It provides an introduction to the Yoruba language, which is spoken by over 30 million people in south-western Nigeria, parts of the Benin Republic, and Togo, as well as in the diaspora populations of Brazil, Cuba, and Haiti. The 15 lessons are designed for both classroom use and self-study. Practice dialogues, combined with grammatical explanations, aid the student in understanding the basics of the language. Each lesson also contains a vocabulary section that highlights the important aspects

Fundamentals of the J Programming Language

And student learning in teacher-authored narratives. Teacher Development, 11(2), 175–188.Article Google Scholar Lantolf, J. P. (2000). Introducing sociocultural theory. Sociocultural Theory and Second Language Learning, 1, 1–26. Google Scholar Lewis, J. (1999). Teacher research and literacy support. Support for Learning, 14(3), 135–143.Article Google Scholar Norton, B. (2000). Identity and language learning: Gender, ethnicity, and educational change. Longman. Google Scholar Okuda, Y. (2016). Naze ima narrative ka?: Sono genjo, haikei, mondai ni tsuite [Why is “narrative” so frequently used and so necessary now?: Actualities, backgrounds and problems]. Research Reports, 18, 67–76. Google Scholar Pavlenko, A., & Lantolf, J. P. (2000). Second language learning as participation and the (re)construction of selves. In J. P. Lantolf (Ed.), Sociocultural theory and second language learning (pp. 155–178). Oxford University Press. Google Scholar Rogoff, B. (2003). The cultural nature of development. Oxford University Press. Google Scholar Sakamoto, N. (2011). Professional development through kizuki—Cognitive, emotional, and collegial awareness. Teacher Development, 15(2), 187–203.Article Google Scholar Talmy, S. (2011). The interview as collaborative achievement: Interaction, identity, and ideology in a speech event. Applied Linguistics, 32(1), 25–42.Article Google Scholar Vygotsky, L. S. (1978). Mind in society: The development of higher psychological processes. Harvard University Press. Google Scholar Vygotsky, L. S. (1986). Thought and language. MIT Press. Google Scholar Witherall, C., & Noddings, N. (Eds.). (1991). Stories lives tell: Narrative and dialogue in education. Teachers Colleague Press. Google Scholar Download referencesAuthor informationAuthors and AffiliationsDepartment of Secondary Education, Okayama University of Science, Kita-ku, Okayama, JapanNami SakamotoAuthorsNami SakamotoYou can also search for this author in PubMed Google ScholarCorresponding authorCorrespondence to Nami Sakamoto .Rights and permissionsCopyright information© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGAbout this chapterCite this chapterSakamoto, N. (2022). Narrative Inquiry as an Inquiry-Based Approach to Teacher Development. In: Teacher Awareness as Professional Development. Palgrave Macmillan, Cham. citation.RIS.ENW.BIBDOI: 09 December 2021 Publisher Name: Palgrave Macmillan, Cham Print ISBN: 978-3-030-88399-7 Online ISBN: 978-3-030-88400-0eBook Packages: Social SciencesSocial Sciences (R0)Publish with us. Visual J (pronounced jay-sharp ) is a discontinued implementation of the J programming language that was a transitional language for programmers of Java and Visual J languages

J Language Cheatsheet - sergeyqz.github.io

Podesva, S. Roberts, & A. Wong (Eds.), Language and sexuality: Contesting meaning in theory and practice (pp. 175–189). Stanford, CA: CSLI Publications. Google Scholar Prewitt-Freilino, J. L., & Bosson, J. K. (2008). Defending the self against identity misclassification. Self and Identity, 7, 168–183. Google Scholar Rieger, G., Linsenmeier, J. A., Gygax, L., Garcia, S., & Bailey, J. M. (2010). Dissecting “gaydar”: Accuracy and the role of masculinity–femininity. Archives of Sexual Behavior, 39, 124–140.PubMed Google Scholar Rule, N. O. (2017). Perceptions of sexual orientation from minimal cues. Archives of Sexual Behavior, 46, 129–139.PubMed Google Scholar Russell, E. (2015). Sounding gay and sounding straight: The performance of male sexual identity in Italian. Journal of Language and Sexuality, 4, 30–76. Google Scholar Russell, E. L. (2017). Style shifting and the phonetic performance of gay vs. straight: A case study from French. Journal of Language and Sexuality, 6, 128–176. Google Scholar Shelp, S. G. (2003). Gaydar. Journal of Homosexuality, 44, 1–14. Google Scholar Smith, H. M., Dunn, A. K., Baguley, T., & Stacey, P. C. (2016). Concordant cues in faces and voices: Testing the backup signal hypothesis. Evolutionary Psychology, 14, 1–10. Google Scholar Smyth, R., Jacobs, G., & Rogers, H. (2003). Male voices and perceived sexual orientation: An experimental and theoretical approach. Language in Society, 32, 329–350. Google Scholar Soliz, J., & Giles, H. (2014). Relational and identity processes in communication: A contextual and meta-analytical review of communication accommodation theory. Annals of the International Communication Association, 38, 107–144. Google Scholar Stuart-Smith, J., Timmins, C., & Wrench, A. (2003). Sex and gender differences in Glaswegian/s/. In M. J. Sole & J. Romero (Eds.), Proceedings of the 15th International Congress of Phonetic Sciences (pp. 1851–1854). New York, NY: Casual Productions Pty Ltd. Google Scholar Suire, A., Tognetti, A., Durand, V., Raymond, M., & Barkat-Defradas, M. (2020). Speech acoustic features: A comparison of gay men, heterosexual men, and heterosexual women. Archives of Sexual Behavior. PubMed Google Scholar Sulpizio, S., Fasoli, F., Antonio, R., Eyssel, F., Paladino, M. P., & Diehl, C. (2020). Auditory gaydar: Perception of sexual orientation based on female voice. Language and Speech, 63, 184–206.PubMed Google Scholar Sulpizio, S., Fasoli, F., Maass, A., Paladino, M. P., Vespignani, F., Eyssel, F., & Bentler, D. (2015). Acoustic gaydar: Voice-based categorization of speakers’ sexual orientation within and across languages. PLoS ONE, 10, e0128882. PubMed PubMed Central Google Scholar Sylva, D., Rieger, G., Linsenmeier, J. A., & Bailey, J.

The J Programming Language - InfoQ

Y, (2014) Empirical evaluation of gated recurrent neural networks on sequence modeling. arXiv preprint arXiv:1412.3555Cohen WW (1996) Learning rules that classify e-mail. In AAAI spring symposium on machine learning in information access (Vol. 18, p. 25)Cohen PR, Morgan J, Ramsay AM (2002) Intention in communication, Am J Psychol 104(4)Collobert R, Weston J (2008) A unified architecture for natural language processing. In proceedings of the 25th international conference on machine learning (pp. 160–167)Dai Z, Yang Z, Yang Y, Carbonell J, Le QV, Salakhutdinov R, (2019) Transformer-xl: attentive language models beyond a fixed-length context. arXiv preprint arXiv:1901.02860Davis E, Marcus G (2015) Commonsense reasoning and commonsense knowledge in artificial intelligence. Commun ACM 58(9):92–103Article Google Scholar Desai NP, Dabhi VK (2022) Resources and components for Gujarati NLP systems: a survey. Artif Intell Rev:1–19Devlin J, Chang MW, Lee K, Toutanova K, (2018) Bert: pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805Diab M, Hacioglu K, Jurafsky D (2004) Automatic tagging of Arabic text: From raw text to base phrase chunks. In Proceedings of HLT-NAACL 2004: Short papers (pp. 149–152). Assoc Computat LinguistDoddington G (2002) Automatic evaluation of machine translation quality using n-gram co-occurrence statistics. In proceedings of the second international conference on human language technology research (pp. 138-145). Morgan Kaufmann publishers IncDrucker H, Wu D, Vapnik VN (1999) Support vector machines for spam categorization. IEEE Trans Neural Netw 10(5):1048–1054Article Google Scholar Dunlavy DM, O’Leary DP, Conroy JM, Schlesinger JD (2007) QCS: A system for querying, clustering and summarizing documents. Inf Process Manag 43(6):1588–1605Article

The J programming language from J Software - GitHub

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