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Journal articles on the topic 'Machine-generated works'

Author: Grafiati

Published: 2 June 2025

Last updated: 31 July 2025

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1

Wang, Faye F. "Copyright Protection for AI-Generated Works." Amicus Curiae 5, no. 1 (2023): 88–103. http://dx.doi.org/10.14296/ac.v5i1.5663.

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Since the 2010s, artificial intelligence (AI) has quickly grown from another subset of machine learning (ie deep learning) in particular with recent advances in generative AI, such as ChatGPT. The use of generative AI has gone beyond leisure purposes. It has now been widely used to generate music, news articles and image-based art works. This prompts a regulatory interpretation as to how AI-generated works should be appropriately used to eliminate their potential harm to society, but at the same time how it should be protected to foster human creativity and promote a well-functioning market. T

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de Vries, Erik, Martijn Schoonvelde, and Gijs Schumacher. "No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications." Political Analysis 26, no. 4 (2018): 417–30. http://dx.doi.org/10.1017/pan.2018.26.

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Automated text analysis allows researchers to analyze large quantities of text. Yet, comparative researchers are presented with a big challenge: across countries people speak different languages. To address this issue, some analysts have suggested using Google Translate to convert all texts into English before starting the analysis (Lucas et al. 2015). But in doing so, do we get lost in translation? This paper evaluates the usefulness of machine translation for bag-of-words models—such as topic models. We use the europarl dataset and compare term-document matrices (TDMs) as well as topic model

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Zhao, X. M., Y. Z. Bi, and Hai Long Wang. "Collision and Determination of Workpiece Location in Parallel Mechanism Type Machine Tools." Materials Science Forum 471-472 (December 2004): 855–59. http://dx.doi.org/10.4028/www.scientific.net/msf.471-472.855.

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This paper uses HexaM made by Toyoda Machine Works Co. as an analytical model of parallel mechanism type machine tools. Much collision may be generated for its complicated structure and working space, using this machine. For example, those collisions are generated between the moving components of the parallel mechanism, between the moving components and a workpiece and between the moving components and the tool storage for automatic changing. In this paper, an algorithm for detecting and avoiding those collisions is proposed. The method for avoiding the collisions is conducted by readjusting t

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Liu, Yiqian. "The Copyright Protection Issue of Artificial Intelligence-Generated Creations: A Dialectical Analysis of Law and Practice." Lecture Notes in Education Psychology and Public Media 34, no. 1 (2024): 55–63. http://dx.doi.org/10.54254/2753-7048/34/20231891.

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This article discusses the issue of the nature of works generated by artificial intelligence similar to ChatGPT and the protection of copyright laws. The author first analyzes the content of artificial intelligence generated products or the provisions on the nature of works in major national laws. This article further analyzes the legal practices of various countries in the qualitative analysis of artificial intelligence generated works based on cases of artificial intelligence copyright in different countries. We explored two theoretical perspectives regarding the copyrightability of two type

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Young, Victoria. "An Unauthorized Renaissance? An Analysis of Artists’ Claims for Copyright Infringement Against AI Generated Art and Possible Defenses." FIU Law Review 18, no. 2 (2024): 521–43. http://dx.doi.org/10.25148/lawrev.18.2.14.

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AI currently presents a novel issue in terms of copyright infringement, specifically AI generated art. Recently, a group of artists filed a class action lawsuit against several AI generated art companies. This comment evaluates the potential avenues the court may take. The artists allege these AI generated art companies directly infringed on their copyrighted works by making unauthorized copies of copyrighted works which they used to train their machine learning programs. A determination on whether AI generated art constitutes copyright infringement has not been made by the courts before. To b

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Ms., Alakammai G., and Aparna Kumari Ms. "Navigating the Nexus of Creativity and Code: Legal Challenges and Opportunities in AI-Generated Copyright." Annual International Journal on Analysis of Contemporary Legal Affairs (AIJACLA) 5 (May 25, 2025): 95–111. https://doi.org/10.5281/zenodo.15514171.

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<em>The advent of AI has blurred the boundaries between human ingenuity and machine precision. The incapacity of artificial intelligence to understand the essence of creativity has had a profound effect on the creative process, impacting the creation of artistic, musical, and literary works. With the advancement of AI, it has become increasingly difficult to distinguish between human creativity and computer accuracy. This paper titled, “ Navigating the Nexus of Creativity and Code: Legal Challenges and Opportunities in AI-Generated Copyright”, delves into the delicate aspects of cr

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AnnisaPutri, Tasya Raehan, Hari Sutra Disemadi, and Rufinus Hotmaulana Hutauruk. "The Ethical Challenges in Copyright AI Creativity." Jurnal Media Hukum 13, no. 1 (2025): 1–15. https://doi.org/10.59414/jmh.v13i1.756.

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n this era of rapid technological advancement, marked by the emergence of Artificial Intelligence (AI), the legal status surrounding AI-generated works sparks considerable debate. The question arises: do these creations warrant legal protection? This study employs a normative legal research method, drawing upon statutory regulations. Findings indicate that AI cannot be deemed a legal subject with rights and obligations, as it is a product of human-made technology. Consequently, AI-generated works do not meet the criteria for copyright protection, namely fixation, originality, and minimal creat

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Wu, Yihan, Junliang Guo, Xu Tan, et al. "VideoDubber: Machine Translation with Speech-Aware Length Control for Video Dubbing." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 11 (2023): 13772–79. http://dx.doi.org/10.1609/aaai.v37i11.26613.

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Video dubbing aims to translate the original speech in a film or television program into the speech in a target language, which can be achieved with a cascaded system consisting of speech recognition, machine translation and speech synthesis. To ensure the translated speech to be well aligned with the corresponding video, the length/duration of the translated speech should be as close as possible to that of the original speech, which requires strict length control. Previous works usually control the number of words or characters generated by the machine translation model to be similar to the s

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Koyano, Hitoshi, Morihiro Hayashida, and Tatsuya Akutsu. "Maximum margin classifier working in a set of strings." Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences 472, no. 2187 (2016): 20150551. http://dx.doi.org/10.1098/rspa.2015.0551.

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Numbers and numerical vectors account for a large portion of data. However, recently, the amount of string data generated has increased dramatically. Consequently, classifying string data is a common problem in many fields. The most widely used approach to this problem is to convert strings into numerical vectors using string kernels and subsequently apply a support vector machine that works in a numerical vector space. However, this non-one-to-one conversion involves a loss of information and makes it impossible to evaluate, using probability theory, the generalization error of a learning mac

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Zimmermann, Wolf, and Thilo Gaul. "On the Construction of Correct Compiler Back-Ends: An ASM-Approach." JUCS - Journal of Universal Computer Science 3, no. (5) (1997): 504–67. https://doi.org/10.3217/jucs-003-05-0504.

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Existing works on the construction of correct compilers have at least one of the following drawbacks: (i) correct compilers do not compile into machine code of existing processors. Instead they compile into programs of an abstract machine which ignores limitations and properties of real-life processors. (ii) the code generated by correct compilers is orders of magnitudes slower than the code generated by unverified compilers. (iii) the considered source language is much less complex than real-life programming languages. This paper focuses on the construction of correct compiler backends which

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Sun, Yingkai. "Analysis Of the Principle and Application of Machine Learning for Music Composition." Highlights in Science, Engineering and Technology 85 (March 13, 2024): 556–62. http://dx.doi.org/10.54097/8yg39t12.

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Contemporarily, machine learning and artificial intelligence are undergoing rapid developments in the field of arts. This paper explores the principles and applications of machine learning in music composition, tracing back to its inception in 1950s, and taking a brief look at the first works in the field of computer-generated music. It delves into the key principles of music composition using machine learning, and discusses the theory behind major models of recurrent neural networks (RNNs) and Generative Adversarial Networks (GANs) and how they are utilized in music composition. This paper al

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Irfan, Zia Ullah, Yabinda Saleem, and Bakht Munir. "Hampering Originality: IP Laws in the USA in the Milieu of Generative Artificial Intelligence." Global Legal Studies Review IX, no. II (2024): 30–37. http://dx.doi.org/10.31703/glsr.2024(ix-ii).04.

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Artificial Intelligence (AI) is leading to the replacement of human intellect by Generative Artificial Intelligence (GAI), which is to be a new norm for redefining artistic and literary works not subject to human authorship. The IP laws, particularly in the USA, haven't proved to be dynamic enough to adapt to this fast-moving change. The existing IP laws seem unfair when seen through the prism of the rationale behind the protection of intellectual property in the wake of generative artificial intelligence. The article develops the argument that intellectual property laws especially copyright l

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Yang, Yi. "An Exploration of Generative Artificial Intelligence Authorship Eligibility." GBP Proceedings Series 1 (January 11, 2025): 95–99. https://doi.org/10.71222/v0bafa92.

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In the era of generative artificial intelligence, whether machine creations have copyright is a controversial issue. This paper takes the first domestic copyright infringement case of artificial intelligence-generated works heard by the Beijing Internet Court as the background, and takes the “creative tool theory” adopted in the judgment as the basis for the copyright infringement case. " is used as the main analytical tool to explore the copyright subject qualification of artificial intelligence. At the same time, the "presumed author theory" is used as a supplementary theory for research. It

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Jiang, Jiacheng. "On the Identification of the Work Nature of Generative Artificial Intelligence Creation." Journal of Intelligence and Knowledge Engineering 1, no. 1 (2023): 62–67. http://dx.doi.org/10.62517/jike.202304110.

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In the new scientific and technological revolution caused by the cognitive paradigm of law and the development of disciplinary update is on the rise, the "generative artificial intelligence" chat machine represented by ChatGPT, Wenxin Yiyi, etc., came into being. Based on the characteristics of "generative artificial intelligence", it is very likely to trigger new thinking about the right attribute and attribution of "Works of art" in the field of copyright." Generative AI" chat machine is a natural language processing tool developed based on deep neural network architecture, which is essentia

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R, Anjeevaragavan. "Multi-Class AI Text Detection Using XAI." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 05 (2025): 1–9. https://doi.org/10.55041/ijsrem48478.

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Abstract— It has become increasingly difficult to recognize whether a piece of writing was created by a human or generated by an AI model. As language models grow more powerful, the gap between machine-generated and human-written text is narrowing fast. This project explores a basic, interpretable machine learning setup that attempts to separate the two. We use a TF-IDF-based feature extraction method along with logistic regression to classify input text. To give more insight into how the system works, we integrate LIME, a tool that explains which words played a role in the final decision.We a

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Alexiadis, Alessio. "Deep Multiphysics and Particle–Neuron Duality: A Computational Framework Coupling (Discrete) Multiphysics and Deep Learning." Applied Sciences 9, no. 24 (2019): 5369. http://dx.doi.org/10.3390/app9245369.

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There are two common ways of coupling first-principles modelling and machine learning. In one case, data are transferred from the machine-learning algorithm to the first-principles model; in the other, from the first-principles model to the machine-learning algorithm. In both cases, the coupling is in series: the two components remain distinct, and data generated by one model are subsequently fed into the other. Several modelling problems, however, require in-parallel coupling, where the first-principle model and the machine-learning algorithm work together at the same time rather than one aft

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DÜZ, Oğuz, Ozan DÜZ, Ozan KARABAŞ, Senai YALÇINKAYA, and Mihalis Michael KUYUCU. "AN EVALUATION OF TECHNOLOGY AND ARTIFICIAL INTELLIGENCE'S RELATIONSHIP WITH CULTURE AND ART IN TERMS OF LITERATURE." INTERNATIONAL REFEREED JOURNAL OF ENGINEERING AND SCIENCES, no. 20 (2023): 39–63. http://dx.doi.org/10.17366/uhmfd.2023.20.3.

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The main purpose of artificial intelligence is to create new works of art by developing the creativity of the machine. However, the idea that the pieces generated by artificial intelligence are evaluated as human imitations and do not reflect art in the true sense has become the focus of discussions. There are many different opinions on whether the works generated by artificial intelligence should be accepted as works of art and whether artificial intelligence has overtaken human artists. Aim: The aim of this study is to review and interpret the literature on artworks prepared and generated us

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Bysaga, Y. M., D. M. Byelov, and V. V. Zaborovskyi. "Artificial intelligence and copyright and related rights." Uzhhorod National University Herald. Series: Law 2, no. 76 (2023): 299–304. http://dx.doi.org/10.24144/2307-3322.2022.76.2.47.

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This article examines the impact of artificial intelligence (AI) on copyright and related rights in the context of today’s digital environment. The growing role of AI in creativity and content creation creates new challenges and questions regarding ownership, authorship and legal protection.The article considers two main aspects: first, how AI affects the creation of creative works, in particular, the generation of texts, music, images and videos without direct human intervention. Second, what are the problems of recognizing the authorship of AI-created works and establishing responsibility fo

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Sim, Cheol-Woong. "A Study of the Characteristics of Image Data Works Generated by a Machine Learning Algorithm - Focused on My Work." CONTENTS PLUS 19, no. 3 (2021): 5–21. http://dx.doi.org/10.14728/kcp.2021.19.03.005.

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Navarro, Ángel, and Francisco Casacuberta. "Neural Models for Measuring Confidence on Interactive Machine Translation Systems." Applied Sciences 12, no. 3 (2022): 1100. http://dx.doi.org/10.3390/app12031100.

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Reducing the human effort performed with the use of interactive-predictive neural machine translation (IPNMT) systems is one of the main goals in this sub-field of machine translation (MT). Prior works have focused on changing the human–machine interaction method and simplifying the feedback performed. Applying confidence measures (CM) to an IPNMT system helps decrease the number of words that the user has to check through the translation session, reducing the human effort needed, although this supposes losing a few points in the quality of the translations. The effort reduction comes from dec

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Baishali, Das. "This Art Is Mine but Also My Ai Application's: Moral and Ethical Consequences of AI Applications in Generating Artistic and Literary Creations." Literary Enigma 1, no. 2 (2025): 106–10. https://doi.org/10.5281/zenodo.15359916.

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Abstract   In the twenty-first century, we have witnessed an exponential rise in the usage and dependency on applications that use Artificial Intelligence to create new trends in art and literature. This project aims to posit the different reactions from the different sections of society and further use them to construct a platform to critically examine the position of such digitally, AI-generated works from an ethically cantered perspective in order to highlight the future implications and consequences of continual usage of these applications in the field of humanities and on the grey ar

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Lopes, Phil, and Georgios Yannakakis. "Investigating Collaborative Creativity via Machine-Mediated Game Blending." Proceedings of the AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment 10, no. 6 (2021): 10–13. http://dx.doi.org/10.1609/aiide.v10i6.12694.

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Can the creativity of humans be enhanced through mutual cooperation, or is it a detriment to their own individual creativity? Although most artists are known for their artistic individuality, some of the best creative works were achieved through mutual collaborative efforts. This paper proposes the study of a game blending system capable of combining user- and machine-generated content from multiple users and creativity facets (e.g. audio, visuals, narrative) for the creation of complete games. Supported by mixed-initiative design tools and human computation (crowdsourcing), users create facet

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Syrbo, Vladislav, and Dar'ya Krapivina. "Transformations of Copyright and Neighboring Rights in the Digital Era." Bulletin of Kemerovo State University. Series: Humanities and Social Sciences 9, no. 1 (2025): 161–72. https://doi.org/10.21603/2542-1840-2025-9-1-161-172.

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The rapid technological development has introduced artificial intelligence (AI) to creative industries. As a result, institutions of copyright and neighboring, or related, rights are facing new challenges that require new legal approaches. The authors examined various copyright issues connected with works of art generated with the help of digital algorithms and machine learning. The analysis of the current laws in this sphere revealed new models that could accommodate the specifics of digital content within the legal domain. The authorship of AI-generated works can belong to the developer of t

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Shen, Hua, and Ting-Hao Huang. "How Useful Are the Machine-Generated Interpretations to General Users? A Human Evaluation on Guessing the Incorrectly Predicted Labels." Proceedings of the AAAI Conference on Human Computation and Crowdsourcing 8 (October 1, 2020): 168–72. http://dx.doi.org/10.1609/hcomp.v8i1.7477.

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Explaining to users why automated systems make certain mistakes is important and challenging. Researchers have proposed ways to automatically produce interpretations for deep neural network models. However, it is unclear how useful these interpretations are in helping users figure out why they are getting an error. If an interpretation effectively explains to users how the underlying deep neural network model works, people who were presented with the interpretation should be better at predicting the model’s outputs than those who were not. This paper presents an investigation on whether or not

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Gue, Ivan Henderson Vy, Alexis Mervin Sy, Ailene Nuñez, Pocholo James Loresco, Jaychris Georgette Onia, and Manuel Belino. "A Rule Induction Framework on the Effect of ‘Negative’ Attributes to Academic Performance." International Journal of Emerging Technologies in Learning (iJET) 16, no. 15 (2021): 31. http://dx.doi.org/10.3991/ijet.v16i15.24269.

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Attaining high retention rates among engineering institutions is a predominant is-sue. A significant portion of engineering students face challenges of retention. Academic advising was implemented to resolve the issue. Decision support sys-tems were developed to support the endeavor. Machine learning have been inte-grated among such systems in predicting student performance accurately. Most works, however, rely on a black box model approach. Rule induction generates simpler if-then rules, exhibiting clearer understanding. As most research works considered attributes for positive academic perfo

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Kokane, Chandrakant Deelip, Kishor R. Pathak, Gopal Mohadikar, Rakhi Subhash Pagar, Suhas Chavan, and Sopan Bapu Kshirsagar. "Machine Learning-Based Sentiment Analysis of Incoming Calls on Helpdesk." International Journal on Recent and Innovation Trends in Computing and Communication 11, no. 9 (2023): 21–27. http://dx.doi.org/10.17762/ijritcc.v11i9.8113.

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In today's daily life we are getting so many anonymous calls. Some calls are related to loan marketing and finance. As per the survey, one person is getting 26% spam calls in a day. The proposed methodology accepts user calls and based on the conversation the spam numbers are identified and the same information is provided to the other callers. This is possible because of machine learning-based sentiment analysis. Sentiment analysis is the subdomain of machine learning. The goal of this research is to propose an adaptive methodology for incoming calls. The sentiment-based incoming calls help d

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T.K, Divyaprashiktha. "DEVELOPMENT OF AI AND ISSUES PERTAINING TO COPYRIGHT- A CRITICAL STUDY." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–13. http://dx.doi.org/10.55041/ijsrem27618.

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Artificial intelligence can be defined as one of the best gifts of science and technology to the mankind. These emerging technologies plays an important role in “revolutionizing the modern world”. Artificial intelligence has made life easy as it has been used and is still in use in different spheres of the evolving human life. Can AI be considered as a “legal person”? Currently, various countries including the UK and USA consider the fact that AI is still dependent on some amount of human input. The extensive usage of generative AI programs leaves us with the question of who, if anyone,may hol

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Howlader, Prottyee, Prapti Paul, Meghana Madavi, Laxmi Bewoor, and V. S. Deshpande. "Fine Tuning Transformer Based BERT Model for Generating the Automatic Book Summary." International Journal on Recent and Innovation Trends in Computing and Communication 10, no. 1s (2022): 347–52. http://dx.doi.org/10.17762/ijritcc.v10i1s.5902.

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Major text summarization research is mainly focusing on summarizing short documents and very few works is witnessed for long document summarization. Additionally, extractive summarization is more addressed as compared with abstractive summarization. Abstractive summarization, unlike extractive summarization, does not only copy essential words from the original text but requires paraphrasing to get close to human generated summary. The machine learning, deep learning models are adapted to contemporary pre-trained models like transformers. Transformer based Language models gaining a lot of atten

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Goodfellow, Paul. "The Distributed Authorship of Art in the Age of AI." Arts 13, no. 5 (2024): 149. http://dx.doi.org/10.3390/arts13050149.

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The distribution of authorship in the age of machine learning or artificial intelligence (AI) suggests a taxonomic system that places art objects along a spectrum in terms of authorship: from pure human creation, which draws directly from the interior world of affect, emotions and ideas, through to co-evolved works created with tools and collective production and finally to works that are largely devoid of human involvement. Human and machine production can be distinguished in terms of motivation, with human production being driven by consciousness and the processing of subjective experience a

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Gilbert M. Arces, Eugene Val C. Mangaoang, Michael Anthony Jay B. Regis, and Jomari Joseph A. Barrera. "Development of a Non-Deterministic Finite Automaton with Epsilon Moves (E − NF A) Generator Using Thompson’s Construction Algorithm." Journal of Science, Engineering and Technology (JSET) 6, no. 1 (2018): 149–59. http://dx.doi.org/10.61569/tvex6p34.

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The study designed and developed a finite state machine generator which converts a regular expression to its equivalent non-deterministic finite automaton with -moves ( − NF A). It implements Thompson’s construction algorithm, an algorithm that works recursively by splitting an expression into its constituent subexpressions, from which the − NF A will be constructed by a set of rules. The system accepts a valid regular expression and rejects an invalid one. The regular expression is then parsed and evaluated by a parse tree to be converted to its equivalent states. The states is then connected

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Pudjiati, Danti, Maria Vincentia Eka Mulatsih, Ilham Ilham, Deta Maria Sri Darta, and Febrio Rahim Suhel. "POETIC TRANSLATION AND STUDENTS’ APPRECIATION THROUGH HUMAN TRANSLATOR AND MACHINE SYSTEM." LLT Journal: A Journal on Language and Language Teaching 27, no. 2 (2024): 1049–61. http://dx.doi.org/10.24071/llt.v27i2.9299.

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The engagement with poetry is a personalized journey that transcends standardized methodologies. Key to this process is complete immersion in the poetic experience, alongside evaluators’ openness to both human and machine-generated translations from Indonesian to English. The overarching goal is to enhance students’ discernment and appreciation of these translated works. The study’s specific objectives involved comparing poetic translation assessments by evaluators for both human translators and machine systems. It was to assess students’ appreciation of poetry through the lens of both transla

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Fernández-Edreira, Diego, Jose Liñares-Blanco, and Carlos Fernandez-Lozano. "Identification of Prevotella, Anaerotruncus and Eubacterium Genera by Machine Learning Analysis of Metagenomic Profiles for Stratification of Patients Affected by Type I Diabetes." Proceedings 54, no. 1 (2020): 50. http://dx.doi.org/10.3390/proceedings2020054050.

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Previous works have reported different bacterial strains and genera as the cause of different clinical pathological conditions. In our approach, using the fecal metagenomic profiles of newborns, a machine learning-based model was generated capable of discerning between patients affected by type I diabetes and controls. Furthermore, a random forest algorithm achieved a 0.915 in AUROC. The automation of processes and support to clinical decision making under metagenomic variables of interest may result in lower experimental costs in the diagnosis of complex diseases of high prevalence worldwide.

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Manaf Isayev, Elvin. "Proqram təminatının tərtibatçısı komputer tərəfindən yaradılan əsərin müəllifi kimi". SCIENTIFIC RESEARCH 10, № 6 (2022): 39–42. http://dx.doi.org/10.36719/2789-6919/10/39-42.

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The rapid advancement of technology in today's world has given rise to new areas of debate. These debates cover a wide range of topics, including the legal sphere, legislation, and legislative methodology. Almost every subject of law, including civil, criminal, administrative, intellectual property, and others, is affected and compelled to adopt as a result of technological advancements. In today's technological age, a machine may produce poetry, music, and even chat, making it more difficult to distinguish them from man-made works. The topic of authorship over results generated by a computer,

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Rghioui, Amine, Jaime Lloret, and Abedlmajid Oumnad. "Big Data Classification and Internet of Things in Healthcare." International Journal of E-Health and Medical Communications 11, no. 2 (2020): 20–37. http://dx.doi.org/10.4018/ijehmc.2020040102.

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Every single day, a massive amount of data is generated by different medical data sources. Processing this wealth of data is indeed a daunting task, and it forces us to adopt smart and scalable computational strategies, including machine intelligence, big data analytics, and data classification. The authors can use the Big Data analysis for effective decision making in healthcare domain using the existing machine learning algorithms with some modification to it. The fundamental purpose of this article is to summarize the role of Big Data analysis in healthcare, and to provide a comprehensive a

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Sonam, Nagar. "Algorithmic Authorship In India: A Doctrinal Analysis Of Challenges In IPR Model In The Age Of Generative Ai- Analysing The Need For Hybrid Authorship Models And Legal Reforms." International Journal of Advance and Applied Research S6, no. 13 (2025): 136–39. https://doi.org/10.5281/zenodo.14912914.

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<em>Artificial intelligence (AI) is fundamentally reshaping creative production by leveraging advanced machine learning paradigms and deep neural networks. This transformation raises intricate legal and ethical questions about intellectual property rights (IPR), particularly regarding the ownership of AI-generated content. In India, where copyright law is deeply rooted in the notion of human authorship, emerging AI technologies—powered by convolutional neural networks, algorithmic transparency, and explainable AI models—challenge traditional legal frameworks.</em> <em>This paper em

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Mujjiga, Srikanth, Vamsi Krishna, Kalyan Chakravarthi, and Vijayananda J. "Identifying Semantics in Clinical Reports Using Neural Machine Translation." Proceedings of the AAAI Conference on Artificial Intelligence 33 (July 17, 2019): 9552–57. http://dx.doi.org/10.1609/aaai.v33i01.33019552.

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Clinical documents are vital resources for radiologists when they have to consult or refer while studying similar cases. In large healthcare facilities where millions of reports are generated, searching for relevant documents is quite challenging. With abundant interchangeable words in clinical domain, understanding the semantics of the words in the clinical documents is vital to improve the search results. This paper details an end to end semantic search application to address the large scale information retrieval problem of clinical reports. The paper specifically focuses on the challenge of

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Nguyen-Minh, Huong, Tung Tran Hoang, and Giang Pham Thanh. "Machine Learning-Based Jamming Detection for Safety Applications in Vehicular Networks: Individual Detection?" Security and Communication Networks 2023 (October 12, 2023): 1–12. http://dx.doi.org/10.1155/2023/8080669.

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Intelligent transportation system (ITS) refers to advanced applications to make transportation safer and more intelligent. The dynamic and diverse natures of the system have been creating many challenges in ITS deployment and security. The progression in recent years of machine learning provides potentially strong methods to exploit data sources from transportation networks. Machine learning-based approaches promise to deal with various challenges in networks thanks to their ability to adapt to the changing network topology and network scale. This paper investigates the application of machine-

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Karimullah, Muhammad Zidan, Ria Wierma Putri, and Rohaini Rohaini. "Hak Cipta atas Hasil Tulisan Kecerdasan Artifisial: Tinjauan Etika Kekayaan Intelektual dan Status Kepemilikannya." AKADEMIK: Jurnal Mahasiswa Humanis 5, no. 2 (2025): 1079–94. https://doi.org/10.37481/jmh.v5i2.1449.

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Artificial Intelligence (AI) technology has ushered the world into a new era in which machines exhibit certain aspects of human intelligence. One such capability of AI is its ability to generate written responses based on commands or inquiries posed by humans, facilitated by natural language processing (NLP). This output raises legal concerns regarding copyright ownership and the ethical considerations surrounding intellectual property. This research examines the ethical dimensions of intellectual property in relation to AI-generated writing and analyzes the status of copyright ownership for w

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Gülzow, Jörg Marvin, and Oliver Deussen. "Region-Based Approaches in Robotic Painting." Arts 11, no. 4 (2022): 77. http://dx.doi.org/10.3390/arts11040077.

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An important aspect of robotic painting is replicating human painting techniques on machines, in order to automatically produce artwork or to interact with a human painter. Usually, painterly rendering techniques are transferred to the machine, and strokes are used as the basic building block of an image, as they can easily be mapped to the robot. In contrast, we propose to consider regions as a basic primitive to achieve more human-like results and to make the painting process more modular. We analyze the works of Kadinsky, Mondrian, Delaunay, and van Gogh to show the basis of region-based te

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Pandey, Dhiraj, Megha Jain, and Kavita Pandey. "An approach for predicting the price of a stock using deep neural network." Journal of Information and Optimization Sciences 44, no. 3 (2023): 529–39. http://dx.doi.org/10.47974/jios-1412.

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For the prediction of any stock price and its fluctuations in prices, researchers have suggested several versions of machine learning techniques. Machine learning-based techniques fail to achieve good prediction and in turn, their accuracy is not adequate to predict the stock price. For sentiment analysis related to the financial domain BERT model is quite useful. The score generated by BERT is useful to get more insight. Few research works which have incorporated financial news, have not used financial corpus for training and testing. FinBERT is quite useful to solve stock pricing fluctuation

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Kim, Yun-Myung. "Data and Fair use." Korea Copyright Commission 141 (March 30, 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.

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Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its wi

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Kim, Yun-Myung. "Data and Fair use." Korea Copyright Commission 141 (March 30, 2023): 5–53. http://dx.doi.org/10.30582/kdps.2023.36.1.5.

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Data collection and use are the beginning and end of machine learning. Looking at ChatGPT, data is making machines comparable to human capabilities. Commercial purposes are not naturally rejected in the judgment of fair use of the process of producing or securing data for system learning. The UK, Germany, and the EU are also introducing copyright restrictions for data mining for non-profit purposes such as research studies, and Japan is more active. Japan’s active legislation is the reason why there are no comprehensive fair use regulations like Korea and the United States, but it shows its wi

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Cai, Qiang, Fenghai Li, Yifan Chen, Haisheng Li, Jian Cao, and Shanshan Li. "Label Rectification Learning through Kernel Extreme Learning Machine." Wireless Communications and Mobile Computing 2021 (March 12, 2021): 1–6. http://dx.doi.org/10.1155/2021/6669081.

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Along with the strong representation of the convolutional neural network (CNN), image classification tasks have achieved considerable progress. However, majority of works focus on designing complicated and redundant architectures for extracting informative features to improve classification performance. In this study, we concentrate on rectifying the incomplete outputs of CNN. To be concrete, we propose an innovative image classification method based on Label Rectification Learning (LRL) through kernel extreme learning machine (KELM). It mainly consists of two steps: (1) preclassification, ext

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Chen, Guo Da, Ying Chun Liang, He Ran Wang, Ya Zhou Sun, and Jia Xuan Chen. "Frequency Domain Error Analysis in Ultra-Precision Flycutting." Key Engineering Materials 620 (August 2014): 96–103. http://dx.doi.org/10.4028/www.scientific.net/kem.620.96.

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In some special fields such as precision optics, the part surface has strict requirements on the frequency domain errors, besides the conventional spatial domain errors such as surface roughness error. In light of the available works lacking of the frequency domain error analysis in ultra-precision flycutting, this paper therefore presents its frequency domain error analysis. A case study of KDP crystal flycutting is carried out to show its detailed processes, where the processing parameters, tool geometry, motion dynamic error of the machine guideway and tool-work vibration induced dynamic er

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Chen, Haoyu. "The advance of neural networks generalization performance." Applied and Computational Engineering 5, no. 1 (2023): 818–25. http://dx.doi.org/10.54254/2755-2721/5/20230711.

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A big mystery in deep learning is the promising generalization performance generated by massive neural networks. While over-parameterization increases the tendency of overfitting in other machine learning models, neural networks seem to magically overcome this hurdle and achieve minor test errors in various tasks. Researchers are motivated to resolve this enigma through a variety of aspects and methods, both theoretically and empirically. This paper aims to comprehensively review the explanations for the generalization power of deep networks. Firstly, the review compares various types of gener

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Kumar, A. Kiran. "Analysis and Fabrication of Control Surface of a Missile." Journal for Manufacturing Science and Production 14, no. 1 (2014): 31–38. http://dx.doi.org/10.1515/jmsp-2013-0020.

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AbstractThe aim and objective of the project work is the modeling of control surface which has complex three dimensional aerodynamic profiles using solid works software, performing analysis of the model with the imposed loads and boundary conditions using ANSYS to estimate the maximum stress and deflection. The material used for this component is titanium alloy (Ti-6Al-4V) which has good properties like resistance to corrosion, high strength to weight ratio, which are mostly needed for aerospace vehicles. The actual dimensions of control surface are 350×225×20. The model of the control surface

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Powell, Michael, Jamison A. Rotz, and Kevin D. O’Malley. "How Machine Learning Is Improving U.S. Navy Customer Support." Proceedings of the AAAI Conference on Artificial Intelligence 34, no. 08 (2020): 13188–95. http://dx.doi.org/10.1609/aaai.v34i08.7023.

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The U.S. Navy is successfully using natural language processing (NLP) and common machine-learning (ML) algorithms to categorize and automatically route plain text support requests at a Navy fleet support center. The algorithms enhance routine IT support tasks with automation and reduce the workload of service desk agents. The ML pipeline works in a five-step process. First, an archive of documents is created from various sources, including standard operating procedure (SOP) memos, frequently asked questions (FAQs), knowledge articles, Wikipedia articles, encyclopedia articles, previously close

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Malik, Kainat, Faisal Rehman, Tahir Maqsood, Saad Mustafa, Osman Khalid, and Adnan Akhunzada. "Lightweight Internet of Things Botnet Detection Using One-Class Classification." Sensors 22, no. 10 (2022): 3646. http://dx.doi.org/10.3390/s22103646.

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Like smart phones, the recent years have seen an increased usage of internet of things (IoT) technology. IoT devices, being resource constrained due to smaller size, are vulnerable to various security threats. Recently, many distributed denial of service (DDoS) attacks generated with the help of IoT botnets affected the services of many websites. The destructive botnets need to be detected at the early stage of infection. Machine-learning models can be utilized for early detection of botnets. This paper proposes one-class classifier-based machine-learning solution for the detection of IoT botn

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Laily, Nur, Muktar Redy Susila, Juwita Sari, and Pontjo Bambang Mahargiono. "IMPLEMENTING IOT AND MACHINE LEARNING FOR DISASTER MITIGATION." International Conference of Business and Social Sciences 3, no. 1 (2024): 444. http://dx.doi.org/10.24034/icobuss.v3i1.396.

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This prototype output research has the aim of creating a dashboard that is used to monitor river water levels. The dashboard created will display data in real-time and prediction results. The use of the dashboard is to minimize the risk in the event of flooding caused by river overflow. The way this prototype works is to take data from sensors that have been installed at several points. The recorded data will be stored in a database using the working principles of the Internet of Things. For predictions, machine learning is used to produce future river water level figures. The machine learning

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M Vishwas Bharadwaj, P Shreyas Gowda, Sanjana Desh, and U N Poorvi Vasishta. "Review of Sentiment Analysis in Cryptocurrency Trading." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 2738–46. https://doi.org/10.47392/irjaeh.2025.0406.

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The rapid rise of cryptocurrencies has impacted the global socio-economic landscape, encouraging investors to seek income through crypto trading. Due to the market’s volatility and complex interdependencies, researchers have built various prediction models using machine learning, deep-learning, and sentiment-based hybrid algorithms. Notably, the DLCFS (Deep Learning Cryptocurrency Forecasting considering Sentiment) framework incorporates market features, trading volume, and sentiment from Reddit to improve price predictions for Bitcoin, Ethereum, and Litecoin—achieving high accuracy when compa

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