Relevant bibliographies by topics / Machine-generated works

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Academic literature on the topic 'Machine-generated works'

Author: Grafiati
Published: 2 June 2025
Last updated: 31 July 2025

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Journal articles on the topic "Machine-generated works"

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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, &ldquo; Navigating the Nexus of Creativity and Code: Legal Challenges and Opportunities in AI-Generated Copyright&rdquo;, 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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Books on the topic "Machine-generated works"

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Julesz, Bela. Dialogues on Perception. The MIT Press, 1994. http://dx.doi.org/10.7551/mitpress/2398.001.0001.

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An elucidation of ideas and insights generated by the paradigm of "early vision," presented in the form of dialogues. Renowned for his work in depth perception and pattern recognition, Bela Julesz originated the techniques—involving computer-generated random-dot stereograms, cinematograms, and textures—that resulted in the subfield of human psychology called "early vision." In this book, Julesz elucidates the ideas and insights generated by this exciting paradigm in a series of dialogues between Julesz the naif and Julesz the scientist. These playful, personal, deeply informed dialogues, thoug
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Voutilainen, Atro. Part-of-Speech Tagging. Edited by Ruslan Mitkov. Oxford University Press, 2012. http://dx.doi.org/10.1093/oxfordhb/9780199276349.013.0011.

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This article outlines the recently used methods for designing part-of-speech taggers; computer programs for assigning contextually appropriate grammatical descriptors to words in texts. It begins with the description of general architecture and task setting. It gives an overview of the history of tagging and describes the central approaches to tagging. These approaches are: taggers based on handwritten local rules, taggers based on n-grams automatically derived from text corpora, taggers based on hidden Markov models, taggers using automatically generated symbolic language models derived using
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Perone, James E. Words and Music of David Bowie. Greenwood Publishing Group, Inc., 2007. http://dx.doi.org/10.5040/9798216038382.

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All Music Guide's Stephen Thomas Erlewine has written, Even when he was out of fashion in the '80s and '90s, it was clear that Bowie was one of the most influential musicians in rock, for better or worse. In this comprehensive analysis of David Bowie's career, author James Perone examines the many identities and styles Bowie has developed over the years, and in so doing provides a stunning chronicle of creativity at work. Born David Jones in a London suburb in 1947, David Bowie changed his name in the late '60s to avoid confusion with the singer David Jones of The Monkees. This name change wou
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Ieropoulos, Ioannis A., Pablo Ledezma, Giacomo Scandroglio, Chris Melhuish, and John Greenman. Energy and metabolism. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780199674923.003.0006.

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Energy resulting from metabolism is essential for any living system—from single-cell to multicellular organisms. This also applies to symbiotic robots (SymBots), which function utilizing the energy (electricity) generated by living microorganisms. In the context of living technologies, artificial symbiosis between the living and the artificial entities of the machine becomes vital for the whole system. If the living entity stops generating energy, the mechatronic system ceases to work yet it is the mechatronic system that provides the microbes with food, and gets rid of their waste. This chapt
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O'Sullivan KC, Dominic, Steven Elliott KC, and Rafal Zakrzewski. The Law of Rescission. 3rd ed. Oxford University Press, 2023. http://dx.doi.org/10.1093/law/9780198852285.001.0001.

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Abstract This book, the leading work in the field, is an extensive analysis of the law concerning the rescission of contracts and gifts in England and Wales, and also contains detailed reference to the law of other parts of the Commonwealth including Australia, New Zealand, Canada, British Virgin Islands, Cayman Islands, Isle of Man, Singapore, Hong Kong, Malaysia, and India. The revised third edition builds on the established format, fully updating case law and considering how developments, such as the introduction of machine-generated contracts, impacts on the law. The book also incorporates
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Book chapters on the topic "Machine-generated works"

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Zhong, X., Fujia Yu, and Beichen Xu. "A Human–Machine Collaborative Building Spatial Layout Workflow Based on Spatial Adjacency Simulation." In Computational Design and Robotic Fabrication. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-19-8637-6_2.

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AbstractThe space layout of a reasonable modular building prototype is a time consuming and complex process. Many studies have optimised automatic spatial layouts based on spatial adjacency simulation. Although machine-produced plans satisfy the adjacency and area constraints, people still need further manual modifications to meet other spatially complex design requirements. Motivated by this, we provide a human–machine collaborative design workflow that simulates the spatial adjacency relationship based on physical models. Compared with previous works, our workflow enhances the automated spac
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Borst, Timo, Jonas Mielck, Matthias Nannt, and Wolfgang Riese. "Extracting Funder Information from Scientific Papers - Experiences with Question Answering." In Linking Theory and Practice of Digital Libraries. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-16802-4_24.

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AbstractThis paper is about automatic recognition of entities that funded a research work in economics as being expressed in a publication. While many works apply rules and/or regular expressions to candidate sections within the text, we follow a question answering (QA) based approach to identify those passages that are most likely to inform us about funding. With regard to a digital library scenario, we are dealing with three more challenges: confirming that our approach at least outperforms manual indexing, disambiguation of funding organizations by linking their names to authority data, and
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Wicaksono, Hendro, Tina Boroukhian, and Atit Bashyal. "A Demand-Response System for Sustainable Manufacturing Using Linked Data and Machine Learning." In Dynamics in Logistics. Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-88662-2_8.

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AbstractThe spread of demand-response (DR) programs in Europe is a slow but steady process to optimize the use of renewable energy in different sectors including manufacturing. A demand-response program promotes changes of electricity consumption patterns at the end consumer side to match the availability of renewable energy sources through price changes or incentives. This research develops a system that aims to engage manufacturing power consumers through price- and incentive-based DR programs. The system works on data from heterogeneous systems at both supply and demand sides, which are lin
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Ye, Ying, and Mike Adcock. "Chinese Copyright Law and Computer-Generated Works in the Era of Artificial Intelligence." In Data Science, Machine Intelligence, and Law. Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-50559-2_8.

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Panagiotopoulou, Vasiliki C., Aikaterini Paraskevopoulou, and Panagiotis Stavropoulos. "A Framework to Compute Carbon Emissions Generated from Additive Manufacturing Processes." In Lecture Notes in Mechanical Engineering. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-28839-5_35.

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AbstractAdditive Manufacturing (AM) is an emerging and promising technology increasingly adopted from Industry. However, Industry is responsible for the majority of global carbon emissions (CEs), heavily contributing to greenhouse effect. Therefore, it is important to define the environmental impact of all processes, including AM carbon footprint. This work aims at reviewing literature for the equations for CE calculations of AM and developing a framework for CEs calculations generated from all the types of AM. Literature was found for some AM types, with each type of AM described stepwise and
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Jeyaraj, Manuela Nayantara, and Sarah Jane Delany. "Author Gender Identification Considering Gender Bias." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_17.

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AbstractWriting style and choice of words used in textual content can vary between men and women both in terms of who the text is talking about and who is writing the text. The focus of this paper is on author gender prediction, identifying the gender of who is writing the text. We compare closed and open vocabulary approaches on different types of textual content including more traditional writing styles such as in books, and more recent writing styles used in user generated content on digital platforms such as blogs and social media messaging. As supervised machine learning approaches can re
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Souza, Filipe, Diarmuid Grimes, and Barry O’Sullivan. "A Large Neighborhood Search Approach for the Data Centre Machine Reassignment Problem." In Communications in Computer and Information Science. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26438-2_31.

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AbstractOne of the main challenges in data centre operations involves optimally reassigning running processes to servers in a dynamic setting such that operational performance is improved. In 2012, Google proposed the Machine Reassignment Problem in collaboration with the ROADEF/Euro challenge. A number of complex instances were generated for evaluating the submissions. This work focuses on new approaches to solve this problem.In particular, we propose a Large Neighbourhood Search approach with a novel, domain-specific heuristic for neighborhood selection. This heuristic uses the unbalanced re
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AlAzzam, Bayan A., Manar Alkhatib, and Khaled Shaalan. "Towards Gulf Emirati Dialect Corpus from Social Media." In BUiD Doctoral Research Conference 2023. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-56121-4_27.

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AbstractPurpose: This paper discusses the need for a corpus of Emirati traditional phrases and idioms in natural language processing (NLP) for the Gulf Emirati dialect and its potential applications in fields like voice recognition, machine translation, and sentiment analysis.Methodology: The researchers collected a corpus of more than 3000 traditional Emirati words and idioms by gathering data from several social media platforms, such as forums, YouTube, and Emirati radio stations. In addition, the researchers used the website scraping technologies to collect suitable resources, subsequently
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Evangelista, Adelia, Annalina Sarra, and Tonio Di Battista. "Students’ feedback on the digital ecosystem: a structural topic modeling approach." In Proceedings e report. Firenze University Press and Genova University Press, 2023. http://dx.doi.org/10.36253/979-12-215-0106-3.36.

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Starting from March 2020, strict containment measures against COVID-19 forced the Italian Universities to activate remote learning and supply didactic methods online. This work is aimed at showing students’ perceptions towards a learning-teaching experience practised within a digital learning ecosystem designed in the period of first emergency and then re-proposed for the blended mode. Specifically, students, attending six teaching large courses held by four professors in two different Italian universities, were asked to express their impression in a text guided by questions, requiring the ref
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Sabir, Aqsa, Rahat Hussain, Syed Farhan Alam Zaidi, et al. "Utilizing 360-Degree Images for Synthetic Data Generation in Construction Scenarios." In CONVR 2023 - Proceedings of the 23rd International Conference on Construction Applications of Virtual Reality. Firenze University Press, 2023. http://dx.doi.org/10.36253/10.36253/979-12-215-0289-3.70.

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Computer vision-based safety monitoring requires machine learning models trained on generalized datasets covering various viewpoints, surface properties, and lighting conditions. However, capturing high-quality and extensive datasets for some construction scenarios is challenging at real job sites due to the risky nature of construction scenarios. Previous methods have proposed synthetic data generation techniques involving 2D background randomization with virtual objects in game-based engines. While there has been extensive work on utilizing 360-degree images for various purposes, no study ha
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Conference papers on the topic "Machine-generated works"

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da Costa, Tony, and Matt Barrett. "Improving Cathodic Protection Monitoring Data in the Time of IIoT and Big Data." In CORROSION 2021. AMPP, 2021. https://doi.org/10.5006/c2021-16259.

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ABSTRACT This paper explores the advances in remote monitoring technology and the enhancements that can be made to ensure that pipeline integrity is maximized while operational efficiencies are optimized. Specifically focusing on the data that is generated by cathodic protection and pipeline integrity monitoring devices (e.g. rectifier monitoring), this paper explores how data analytics techniques, such as artificial intelligence and machine learning algorithms, can shine a light on historically ‘dark’ data, improving pipeline integrity operations and the safety of workers and the broader publ
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Farlessyost, William, and Shweta Singh. "Improving Mechanistic Model Accuracy with Machine Learning Informed Physics." In Foundations of Computer-Aided Process Design. PSE Press, 2024. http://dx.doi.org/10.69997/sct.121371.

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Machine learning presents opportunities to improve the scale-specific accuracy of mechanistic models in a data-driven manner. Here we demonstrate the use of a machine learning technique called Sparse Identification of Nonlinear Dynamics (SINDy) to improve a simple mechanistic model of algal growth. Time-series measurements of the microalga Chlorella Vulgaris were generated under controlled photobioreactor conditions at the University of Technology Sydney. A simple mechanistic growth model based on intensity of light and temperature was integrated over time and compared to the time-series data.
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Shahhoseyni, Shabnam, Arijit Chakraborty, Mohammad Reza Boskabadi, Venkat Venkatasubramanian, and Seyed Soheil Mansouri. "Hybrid machine-learning for dynamic plant-wide biomanufacturing." In The 35th European Symposium on Computer Aided Process Engineering. PSE Press, 2025. https://doi.org/10.69997/sct.174465.

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This study focuses on biomanufacturing case study, i.e. Lovastatin production, employing a hybrid modeling framework that combines mechanistic and data-driven approaches. A time-series dataset was generated using the KT-Biologics I (KTB1) plantwide model, a dynamic simulation of continuous biomanufacturing. The dataset captures critical parameters such as nutrient concentrations and API production. The AI-DARWIN framework was used to develop interpretable machine learning models with constrained functional forms, ensuring both accuracy and clarity. The resulting polynomial-based models reveal
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Lai-man Tin, Man. "Generative Adversarial Network Algorithms in Art: Data Video." In Intelligent Human Systems Integration (IHSI 2022) Integrating People and Intelligent Systems. AHFE International, 2022. http://dx.doi.org/10.54941/ahfe100957.

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The recent development of machine learning to synthesize the dataset and manipulate images into new works of art, bringing essential changes in visual art and the method of art creation. The paper aims at applying the Generative Adversarial Network (GAN) to the new media art in particular the image generation and video synthesis through latent space interpolation, through the indirect training in GAN to process a series of still images as the dataset, the generated work presents the ability of machine algorithms in learning and processing the image creation, as well as the next stage of machin
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Mahingoda, Chaga Bihari. "Intellectual Property Rights in the Era of Artificial Intelligence: Navigating the Challenges and Expanding the Boundaries." In SLIIT International Conference on Advancements in Sciences and Humanities 2023. Faculty of Humanities and Sciences, SLIIT, 2023. http://dx.doi.org/10.54389/voaf9040.

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This article delves into the tangled web of artificial intelligence (AI) and intellectual property (IP) rights. It investigates the issues raised by AI generated works such as machine-generated art, music, and literature, as well as the issues of ownership and authorship in these cases. The essay also examines the influence of AI on conventional intellectual property domains such as patents, copyrights, and trademarks, as well as the legal and ethical consequences of AI-driven innovation. This article provides ways for balancing IP protection and supporting innovation in the AI age by studying
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Hurtado, Remigio, Janneth Matute, and Juan Boni. "An analysis model for Machine Learning using Support Vector Machine for the prediction of Diabetic Retinopathy." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001450.

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Diabetic Retinopathy is a public health disease worldwide, which shows that around one percent of the population suffers from this disease. Likewise, another one percent of patients in the population suffer from this disease, but it is not diagnosed. It is estimated that, within three years, millions of people will suffer from this disease. This will increase the percentage of vascular, ophthalmological and neurological complications, which will translate into premature deaths and deterioration in the quality of life of patients. That is why we face a great challenge, which is to predict and d
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Grari, Vincent, Sylvain Lamprier, and Marcin Detyniecki. "Fairness without the Sensitive Attribute via Causal Variational Autoencoder." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/98.

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In recent years, most fairness strategies in machine learning have focused on mitigating unwanted biases by assuming that the sensitive information is available. However, in practice this is not always the case: due to privacy purposes and regulations such as RGPD in EU, many personal sensitive attributes are frequently not collected. Yet, only a few prior works address the issue of mitigating bias in such a difficult setting, in particular to meet classical fairness objectives such as Demographic Parity and Equalized Odds. By leveraging recent developments for approximate inference, we propos
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Tian, Yubao, Yonghong Tang, Zhiheng Wang, and Guang Xi. "Influence of Adjustable Inlet Guide Vanes on the Performance Characteristics of a Shrouded Centrifugal Compressor." In ASME Turbo Expo 2017: Turbomachinery Technical Conference and Exposition. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/gt2017-63918.

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A shrouded centrifugal compressor model stage used for 120,000 m3/h oxygen production air separation unit was designed and tested at several IGV stagger angles from −15° to +60° and machine Mach number from 0.97 to 0.5. Present research works aimed to assess the influence of the adjustable IGVs and the IGV modeling on the shrouded centrifugal compressor performance characteristics and inlet flow field and to explore the effect factors of the CFD prediction accuracy and compressor stability at different IGV stagger angles. The measured results show that the model stage with 0° IGV stagger angle
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Regenwetter, Lyle, and Faez Ahmed. "Design Target Achievement Index: A Differentiable Metric to Enhance Deep Generative Models in Multi-Objective Inverse Design." In ASME 2022 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/detc2022-91344.

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Abstract Deep Generative Machine Learning Models have been growing in popularity across the design community thanks to their ability to learn and mimic complex data distributions. While early works are promising, further advancement will depend on addressing several critical considerations such as design quality, feasibility, novelty, and targeted inverse design. We propose the Design Target Achievement Index (DTAI), a differentiable, tunable metric that scores a design’s ability to achieve designer-specified minimum performance targets. We demonstrate that DTAI can drastically improve the per
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Xiao, Tong, Jiayu Liu, Zhenya Huang, et al. "Learning to Solve Geometry Problems via Simulating Human Dual-Reasoning Process." In Thirty-Third International Joint Conference on Artificial Intelligence {IJCAI-24}. International Joint Conferences on Artificial Intelligence Organization, 2024. http://dx.doi.org/10.24963/ijcai.2024/725.

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Geometry Problem Solving (GPS), which is a classic and challenging math problem, has attracted much attention in recent years. It requires a solver to comprehensively understand both text and diagram, master essential geometry knowledge, and appropriately apply it in reasoning. However, existing works follow a paradigm of neural machine translation and only focus on enhancing the capability of encoders, which neglects the essential characteristics of human geometry reasoning. In this paper, inspired by dual-process theory, we propose a Dual-Reasoning Geometry Solver (DualGeoSolver) to simulate
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Reports on the topic "Machine-generated works"

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Chew, Robert F., Kirsty J. Weitzel, Peter Baumgartner, et al. Improving Text Classification with Boolean Retrieval for Rare Categories: A Case Study Identifying Firearm Violence Conversations in the Crisis Text Line Database. RTI Press, 2023. http://dx.doi.org/10.3768/rtipress.2023.mr.0050.2304.

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Advancements in machine learning and natural language processing have made text classification increasingly attractive for information retrieval. However, developing text classifiers is challenging when no prior labeled data are available for a rare category of interest. Finding instances of the rare class using a uniform random sample can be inefficient and costly due to the rare category’s low base rate. This work presents an approach that combines the strengths of text classification and Boolean retrieval to help learn rare concepts of interest. As a motivating example, we use the task of f
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Adegoke, Damilola, Natasha Chilambo, Adeoti Dipeolu, Ibrahim Machina, Ade Obafemi-Olopade, and Dolapo Yusuf. Public discourses and Engagement on Governance of Covid-19 in Ekiti State, Nigeria. African Leadership Center, King's College London, 2021. http://dx.doi.org/10.47697/lab.202101.

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Numerous studies have emerged so far on Covid-19 (SARS-CoV-2) across different disciplines. There is virtually no facet of human experience and relationships that have not been studied. In Nigeria, these studies include knowledge and attitude, risk perception, public perception of Covid-19 management, e-learning, palliatives, precautionary behaviours etc.,, Studies have also been carried out on public framing of Covid-19 discourses in Nigeria; these have explored both offline and online messaging and issues from the perspectives of citizens towards government’s policy responses such as palliat
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