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Journal articles on the topic 'Generative Principle'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Ahmed, Serwan Samen, and Aram Rasheed Majeed. "Adjective in Linguistic Schools: Traditional, Generative, Cognitive." JOURNAL OF LANGUAGE STUDIES 5, no. 1 (2022): 622–36. http://dx.doi.org/10.25130/jls.5.1.38.

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The research is titled (Adjective in linguistic schools - traditional, generative, Cognitive). The research aims to study the adjective in the Kurdish language as a linguistic category. The types of adjectives were classified according to the linguistic schools, based on many principles. The traditional school concerned with the semantic principle to determine the class of adjectives. In the Generative school, the principle of position and function were two basic principles for defining an adjective. In the Cognitive theory, the principles of the adjective-noun relationship, the position of th

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Ke, Yan, Min-qing Zhang, Jia Liu, Ting-ting Su, and Xiao-yuan Yang. "Generative steganography with Kerckhoffs’ principle." Multimedia Tools and Applications 78, no. 10 (2018): 13805–18. http://dx.doi.org/10.1007/s11042-018-6640-y.

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Yan, Tingting. "Research on Personal Information Protection in the Context of Generative Artificial Intelligence." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (2024): 116–24. http://dx.doi.org/10.54691/vhyxgj39.

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Personal information is the foundation of generative AI, and ChatGPT-like generative AI needs to process a large amount of personal information at various stages such as model training, model generation, and model optimization, which also has a certain impact on traditional personal information protection rules. During the information collection phase, generative AI may fugitive the informed consent rules and infringe on the privacy rights of information subjects. In the information utilization stage, generative AI may impact basic personal information processing rules such as the principle of

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Cheng, Chung-Ying. "Phenomenology and Onto-Generative Hermeneutics: Convergencies." Journal of Chinese Philosophy 42, no. 1-2 (2015): 221–41. http://dx.doi.org/10.1163/15406253-0420102015.

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In examining phenomenology as a base onto-generative hermeneutics (onto-hermeneutics) I find the gradual movement from pure phenomenology in Husserl to an ontological phenomenology in Merleau-Ponty through Heidegger and Gadamer. I argue thus that there is an implicit connection between the phenomenological and the ontological. In order to bring out the desirable connection between the two we must have hermeneutic interpretation of one in terms of the other. This leads to the idea of onto-hermeneutic circle of phenomenology and ontology based on the integration of the four phenomenologies which

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Franke, Herbert W. "Mathematics As an Artistic-Generative Principle." Leonardo. Supplemental Issue 2 (1989): 25. http://dx.doi.org/10.2307/1557939.

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Karimi, Mostafa, Arman Hasanzadeh, and Yang Shen. "Network-principled deep generative models for designing drug combinations as graph sets." Bioinformatics 36, Supplement_1 (2020): i445—i454. http://dx.doi.org/10.1093/bioinformatics/btaa317.

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Abstract Motivation Combination therapy has shown to improve therapeutic efficacy while reducing side effects. Importantly, it has become an indispensable strategy to overcome resistance in antibiotics, antimicrobials and anticancer drugs. Facing enormous chemical space and unclear design principles for small-molecule combinations, computational drug-combination design has not seen generative models to meet its potential to accelerate resistance-overcoming drug combination discovery. Results We have developed the first deep generative model for drug combination design, by jointly embedding gra

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Newmeyer, Frederick J. "Competence vs. performance; theoretical vs. applied." Historiographia Linguistica 17, no. 1-2 (1990): 167–81. http://dx.doi.org/10.1075/hl.17.1-2.13new.

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Summary The past 30 years have seen marked shifts in the generative grammarians’ view of the nature of linguistic competence. The rule-oriented period of early Transformational Grammar, which was ushered in by the publication of Chomsky’s Syntactic Structures in 1957, gave way a decade later to the principle-oriented period of Generative Semantics. By the mid-1970s, the rule-oriented Lexicalist framework had replaced Generative Semantics. Since around 1981, the principle-oriented Principles & Parameters approach is the one to which a majority of generative syntacticians hold allegiance. Ea

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Cengiz Tırpan, Esra. "The ethical issues in generative artificial intelligence: A systematic review." Business & Management Studies: An International Journal 12, no. 4 (2024): 729–47. https://doi.org/10.15295/bmij.v12i4.2431.

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As generative artificial intelligence (generative AI) technology rapidly develops, new tools are being introduced to the market, and its use in many areas, from education to healthcare, is quickly increasing. Therefore, ethical research must keep pace with these developments and address the new challenges. In this way, AI can benefit society and prevent potential harm. This study was conducted to identify ethical issues in the use of generative AI, highlight prominent issues, and provide an overview through a systematic literature review. A systematic search was conducted in Scopus, Web of Sci

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Liu, Qiaochu. "The Application of Generative AI in the Practical Teaching of Environmental Design." Region - Educational Research and Reviews 6, no. 12 (2024): 19. https://doi.org/10.32629/rerr.v6i12.2990.

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With the rapid development of artificial intelligence technology, generative AI, as an emerging technology, is gradually penetrating into various fields including environmental design practice teaching. As a comprehensive subject integrating art, science and technology, environmental design practice teaching is very important for cultivating students' innovative thinking and practical ability. Generative AI has brought unprecedented opportunities for environmental design practice teaching with its powerful content generation ability. However, how to effectively introduce this technology into t

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Kuznetsov, Sergey. "Using Bellman Optimality Principle for the Generative Autoencoder Architecture for the Problems of the Attribute Data Typesetting and Semantic Description in Data Management." Differential Equations and Control Processes, no. 2 (2024): 171–82. https://doi.org/10.21638/11701/spbu35.2024.207.

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The publication presents the problems of identifying data types (typesetting) and semantic description of the attributes when managing structured data and master data (Master Data Management). A formal definition of the generalized attribute typesetting problem is given, which allows generation of the additional data types. This problem allows using the discrete Bellman optimality principle under special criteria of the target function. A unified architecture of the deep generative neural network addressing simultaneously the generalized attribute typesetting and semantic description generatio

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Tanguiane, A. S. "A Principle of Correlativity of Perception and Its Application to Music Recognition." Music Perception 11, no. 4 (1994): 465–502. http://dx.doi.org/10.2307/40285634.

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Correlativity of perception is defined as a capacity to discover similar configurations of stimuli and to form high- level configurations from them. It is equivalent to describing information in terms of generative elements and their transformations. Such a representation saves memory and reveals causality in data generation. This approach is implemented in a model of artificial perception wherein data are selforganized in order to segregate patterns before recognizing them. Input information is described as generative patterns and their transformations. The least complex data representation t

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Li, Wei, and Yongchuan Tang. "Soft Generative Adversarial Network: Combating Mode Collapse in Generative Adversarial Network Training via Dynamic Borderline Softening Mechanism." Applied Sciences 14, no. 2 (2024): 579. http://dx.doi.org/10.3390/app14020579.

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In this paper, we propose the Soft Generative Adversarial Network (SoftGAN), a strategy that utilizes a dynamic borderline softening mechanism to train Generative Adversarial Networks. This mechanism aims to solve the mode collapse problem and enhance the training stability of the generated outputs. Within the SoftGAN, the objective of the discriminator is to learn a fuzzy concept of real data with a soft borderline between real and generated data. This objective is achieved by balancing the principles of maximum concept coverage and maximum expected entropy of fuzzy concepts. During the early

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Yang, Zhi, and Yuanhong Xu. "Study on the Path of Generative Artificial Intelligence Copyright Protection under the Strategy of Intellectual Property Power." Scientific Journal Of Humanities and Social Sciences 6, no. 7 (2024): 172–79. http://dx.doi.org/10.54691/my07eq41.

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Generative AI copyright protection is in response to the inherent requirements of AI development and intellectual property protection. At present, generative AI copyright protection faces problems such as insufficient legal basis for prevention and control mechanisms, unsmooth preventive systems, and poor operation mechanisms. Therefore, to solve the dilemma of generative AI copyright protection, it is necessary to take the principle of risk prevention as the concept, clarify the legal basis for generative AI copyright prevention, reasonably define the scope of generative AI, establish a multi

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Peng, Yingying. "A Comparative Analysis Between GAN and Diffusion Models in Image Generation." Transactions on Computer Science and Intelligent Systems Research 5 (August 12, 2024): 189–95. http://dx.doi.org/10.62051/0f1va465.

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In the field of artificial intelligence, image-generation techniques have been a hotspot for research. Two generative models that have garnered a lot of attention are diffusion models and generative adversarial networks (GANs). This review paper aims to compare and analyze GAN and Diffusion Models in the field of picture generation, as well as to give a thorough discussion of their features, applications, benefits, and drawbacks. Firstly, the work related to the working principle of GAN and diffusion models are introduced, and then their applications and results in image generation are reviewe

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Bayuningrat, Saka Adjie, Moch Zairul Alam, and Diah Pawestri Maharani. "Bentuk Internalisasi Nilai Etik Mengenai Bias Negatif dan Diskriminasi Dalam Platform generative AI." RechtJiva 1, no. 1 (2024): 1–22. http://dx.doi.org/10.21776/rechtjiva.v1n1.1.

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This research discusses a form of classification in the realm of Artificial Intelligence, namely generative AI. Generative AI is an AI technology that can create new content in the form of text, images, audio, and others. However, generative AI has the potential to cause problems such as negative bias and unintentional discrimination in the form of negative stereotyping of a racial group. Current regulations are inadequate to address these challenges and determine the liability of parties in the event of harm. This research aims to analyze the urgency of new regulations related to generative A

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Laine, Joakim, Matti Minkkinen, and Matti Mäntymäki. "Understanding the Ethics of Generative AI: Established and New Ethical Principles." Communications of the Association for Information Systems 56, no. 1 (2025): 1–25. https://doi.org/10.17705/1cais.05601.

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This scoping review develops a conceptual synthesis of the ethics principles of generative artificial intelligence (GenAI) and large language models (LLMs). In regard to the emerging literature on GenAI, we explore 1) how established AI ethics principles are presented and 2) what new ethical principles have surfaced. The results indicate that established ethical principles continue to be relevant for GenAI systems but their salience and interpretation may shift, and that there is a need to recognize new principles in these systems. We identify six GenAI ethics principles: 1) respect for intell

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Jeong, Younhee, and Hui-Jeong Yun. "An Analysis of University Students’ Experiences and Perceptions of Using Text-Generating AI in Assignment Writing: Focusing on the Perspective of the Theory of Education-as-the-Principle." Korean Society for the Study of Educational Principles 30, no. 1 (2025): 67–110. https://doi.org/10.19118/edp.2025.30.1.67.

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This study empirically analyzed university students’ experiences and perceptions regarding the use of text-generating artificial intelligence (AI) and examined them through the lens of the theory of education-as-the-principle(교육본위론). In particular, the study aimed to explore the impact of generative AI use in assignment writing not merely as a learning aid, but as a force influencing education itself—specifically or their self-directed educational posture. A survey was conducted among students enrolled in four-year universities in South Korea, and statistical analyses were performed on 423 val

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Liu, Lanting. "Constructing a Multi-Dimensional Governance System for the Application of Generative AI in Schools: Policy Analysis and Implications of the “Australian Framework for Generative AI in Schools”." Journal of Contemporary Educational Research 9, no. 1 (2025): 157–62. https://doi.org/10.26689/jcer.v9i1.9540.

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With the rapid development of generative artificial intelligence (AI) technology in the field of education, global educational systems are facing unprecedented opportunities and challenges, urgently requiring the establishment of comprehensive, flexible, and forward-looking governance solutions. The “Australian Framework for Generative AI in Schools” builds a multi-dimensional governance system covering aspects such as teaching and humanistic care, fairness and transparency, and accountability and security. Based on 22 specific principles and six core elements, it emphasizes a human-centered d

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Yunqian, Zhang. "AI-DRIVEN BACKGROUND GENERATION FOR MANGA ILLUSTRATIONS: A DEEP GENERATIVE MODEL APPROACH." ORESTA 7, no. 1 (2024): 158–75. https://doi.org/10.5281/zenodo.15080622.

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<em>This paper introduces the generation technique of manga illustration background, discusses the traditional background generation technique and stylized migration technique, and points out that the application of AI technology provides new possibilities for the creation of manga illustration backgrounds. Aiming at the limitations of traditional methods, the design concept and principle of conditional generative model based on deep learning are proposed, the implementation principles of convolutional neural network and generative adversarial network are introduced, and the conditional manga

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Lye, Che Yee, and Lyndon Lim. "Generative Artificial Intelligence in Tertiary Education: Assessment Redesign Principles and Considerations." Education Sciences 14, no. 6 (2024): 569. http://dx.doi.org/10.3390/educsci14060569.

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The emergence of generative artificial intelligence (AI) such as ChatGPT has sparked significant assessment concerns within tertiary education. Assessment concerns have largely revolved around academic integrity issues among students, such as plagiarism and cheating. Nonetheless, it is also critical to consider that generative AI models trained on information retrieved from the Internet could produce biased and discriminatory outputs, and hallucination issues in large language models upon which generative AI acts provide made-up and untruthful outputs. This article considers the affordances an

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Dhawan, Ravi, Kendall Douglas Brooks, Orr Shauly, Denys Shay, and Albert Losken. "Ethical Considerations for Generative Artificial Intelligence in Plastic Surgery." Plastic and Reconstructive Surgery - Global Open 13, no. 6 (2025): e6825. https://doi.org/10.1097/gox.0000000000006825.

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Summary: The integration of artificial intelligence (AI) into surgical care is rapidly transforming healthcare by enhancing efficiency, clinical decision-making, and patient outcomes. Generative AI (genAI), a subfield using large language models such as ChatGPT, Bard, and Midjourney, holds significant promise in automating tasks such as surgical planning and discharge summaries. However, it raises concerns about misinformation, data breaches, biases, and misuse. No genAI technology has yet received Food and Drug Administration approval for surgical use, emphasizing the need for thorough regula

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Keum, Sunyoung, Zhenyan Li, and Minji Kim. "Metaverse Design Principle for Academic Counseling Room Using Generative AI." Journal of Educational Technology 40, no. 3 (2024): 613–41. http://dx.doi.org/10.17232/kset.40.3.613.

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Wang, Xinghao. "Analysis and Application Research of InfoGAN." Highlights in Science, Engineering and Technology 57 (July 11, 2023): 21–26. http://dx.doi.org/10.54097/hset.v57i.9891.

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Generative Adversarial Networks (GAN) have been a revolutionary development in the field of Artificial Intelligence, particularly in the domain of generative models. The information maximizing generative adversarial nets, or infoGAN, is one of the most recent and promising developments in the world of GAN. InfoGAN focuses on maximizing the mutual information between the generator's output and some input variables. This means that the generated images or data can be controlled more easily while maintaining high-quality results.Apart from these applications, infoGAN has also been used in other a

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Dumitru, Alexandra-Mihaela, Sorin Anagnoste, and Marco Savastano. "Unleashing the potential: harnessing generative artificial intelligence for empowering model training." Proceedings of the International Conference on Business Excellence 18, no. 1 (2024): 3618–35. http://dx.doi.org/10.2478/picbe-2024-0294.

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Abstract Recent strides in generative artificial intelligence, particularly large language models, have been propelled by foundation models – learning algorithms trained on extensive and diverse datasets encompassing various subjects. This technology, inspired by the complexity of the human brain, unveils a new frontier in generative Artificial Intelligence (AI), showing its potential in creativity by generating innovative content based on absorbed data and user prompts. It is forecasted that the conversational AI and virtual assistant segment is experiencing the highest growth rate within the

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Tomar, Snehal Singh, Maitreya Suin, and A. N. Rajagopalan. "Exploring the Effectiveness of Mask-Guided Feature Modulation as a Mechanism for Localized Style Editing of Real Images (Student Abstract)." Proceedings of the AAAI Conference on Artificial Intelligence 37, no. 13 (2023): 16350–51. http://dx.doi.org/10.1609/aaai.v37i13.27035.

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The success of Deep Generative Models at high-resolution image generation has led to their extensive utilization for style editing of real images. Most existing methods work on the principle of inverting real images onto their latent space, followed by determining controllable directions. Both inversion of real images and determination of controllable latent directions are computationally expensive operations. Moreover, the determination of controllable latent directions requires additional human supervision. This work aims to explore the efficacy of mask-guided feature modulation in the laten

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Seo, Ji-Young. "College Students’ Preferences on Principles for the Effective Instructional Video Design for Online General English Classes in Korea." Electronic Journal of e-Learning 20, no. 3 (2022): pp313–324. http://dx.doi.org/10.34190/ejel.20.3.2336.

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The present study investigates the preferences of students regarding the principles for the effective design of instructional videos to identify factors that influence engagement. A questionnaire was distributed to 232 students enrolled in online liberal arts classes at a private university in South Korea. Frequency analysis was conducted to determine preferences, whereas an independent sample t-test and one-way analysis of variance were administered to verify any differences in preferences according to gender and grade. The findings are as follows. First, out of the 12 principles that should

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Hu, Ling, Shu-Hao Wu, Weizhou Cai, et al. "Quantum generative adversarial learning in a superconducting quantum circuit." Science Advances 5, no. 1 (2019): eaav2761. http://dx.doi.org/10.1126/sciadv.aav2761.

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Generative adversarial learning is one of the most exciting recent breakthroughs in machine learning. It has shown splendid performance in a variety of challenging tasks such as image and video generation. More recently, a quantum version of generative adversarial learning has been theoretically proposed and shown to have the potential of exhibiting an exponential advantage over its classical counterpart. Here, we report the first proof-of-principle experimental demonstration of quantum generative adversarial learning in a superconducting quantum circuit. We demonstrate that, after several rou

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Wang, Chuang, Jianwen Song, and Lijun Wang. "P‐2.9: A review of image generation methods based on deep learning." SID Symposium Digest of Technical Papers 54, S1 (2023): 507–12. http://dx.doi.org/10.1002/sdtp.16343.

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Image as a medium of visual information transmission have the advantages of vividness, intuition and easy understanding. They play an important role in information transmission and utilization. In recent years, due to the rapid development of deep learning technology in the field of image processing, image generative model based on neural network has become one of the current research hotspots. In the field of deep learning, unsupervised learning model has received more and more attentions, especially in the field of deep generative models, which has made breakthrough progress [1] . Among them

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Sosna, Nina. "From Analytical Аnthropology to Generative Anthropology". Chelovek 32, № 5 (2021): 103. http://dx.doi.org/10.31857/s023620070017441-2.

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This article discusses the role of the technical in V.A. Podoroga&apos;s project of studying the literature worlds as archives of human experience. On the level of content, among many other components of these worlds he distinguished working and non-working machines, “gizmos” and various optical devices, including the mechanic eye, camera, mounting, etc. Formally, the action of these machines can be assessed as alienation, though in the context of modern media studies and exploration of the perspectives of anthropology, they can also be described as a problematic contact zone between the h

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ТІТОВ, Іван Геннадійович. "МЕТОДОЛОГІЧНІ ПРИНЦИПИ ПОСТНЕКЛАСИЧНОЇ ПСИХОЛОГІЇ". Психологія і особистість 1, № 15 (2019): 3–34. https://doi.org/10.5281/zenodo.2559611.

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<em>The transformation of psychological science at postnonclassical stage of its development demands theoretical and methodological reflection of the conceptual grounds of psychology. In this article, we have attempted to outline themost significant principles regarded to postnonclassicalpsychology such as 1) structural-genetic principle; 2) principle of subject (personal agency); 3) principle of orientation on cultural context; 4) principle of ontological constructivism; 5) principle of integration.</em> <em>Structural-genetic principlegrounds the view on psychologica

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Salvucci, Peter. "Multistability and Complex Isotopy in Makam Music Analysis: The Link Between Cognition and Semiotics in Aesthetics of Turkish Music." Roczniki Humanistyczne 72, no. 12 (2024): 207–23. https://doi.org/10.18290/rh247212.13.

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Multistability in perceptual psychology involves ambiguity or rivalry between two or more percepts of sensory stimuli. This phenomenon can be compared to the Greimassian concept of complex isotopy, where multiple terms of semantic content are simultaneously signified in the same reading of a text, providing multiple interpretational meanings. This study proposes that a key aesthetic principle of classical Turkish makam music involves the multistable perception of musical events, which in the semantic sense manifest as interoceptive, complex isotopies. The aesthetic of multiple subjective inter

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Machueva, D. A., D. R. Baraev, and T. M.-A. Bechurkaev. "VARIOUS ASPECTS OF THE WORK OF GENERATIVE INTELLIGENCE IN THE FINE ARTS USING THE DALL-E NEURAL NETWORK AS AN EXAMPE." Vestnik komp'iuternykh i informatsionnykh tekhnologii, no. 243 (September 2024): 20–25. https://doi.org/10.14489/vkit.2024.09.pp.020-025.

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Neural creativity, or Artificial Intelligence Art, is becoming increasingly widespread, competing with traditional works of art created by hand. The study is aimed at analyzing the operating principles and capabilities of neural networks that generate images, as well as assessing their advantages and limitations in various fields of application using the DALL-E neural network as an example. The analysis of domestic and foreign literature in the field of artificial intelligence (AI) technologies and law, structuring and systematization of the received information, is included. The principles of

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Wang, Liwei, Xiong Li, Zhuowen Tu, and Jiaya Jia. "Discriminative Clustering via Generative Feature Mapping." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1162–68. http://dx.doi.org/10.1609/aaai.v26i1.8305.

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Existing clustering methods can be roughly classified into two categories: generative and discriminative approaches. Generative clustering aims to explain the data and thus is adaptive to the underlying data distribution; discriminative clustering, on the other hand, emphasizes on finding partition boundaries. In this paper, we take the advantages of both models by coupling the two paradigms through feature mapping derived from linearizing Bayesian classifiers. Such the feature mapping strategy maps nonlinear boundaries of generative clustering to linear ones in the feature space where we expl

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Gao, Jingjing. "Generative adversarial network based image inpainting." Applied and Computational Engineering 5, no. 1 (2023): 93–98. http://dx.doi.org/10.54254/2755-2721/5/20230540.

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Image inpainting, which is the repair of pixels in damaged areas of an image to make it look as much like the original image as possible. Deep learning-based image inpainting technology is a prominent area of current research interest. This paper focuses on a systematic and comprehensive study of GAN-based image inpainting and presents an analytical summary. Firstly, this paper introduces GAN, which includes the principle of GAN and its mathematical expression. Secondly, the recent GAN-based image inpainting algorithms are summarized, and the advantages and disadvantages of each algorithm are

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Lei, Feifei, Jieren Cheng, Yue Yang, Xiangyan Tang, Victor S. Sheng, and Chunzao Huang. "Improving Heterogeneous Network Knowledge Transfer Based on the Principle of Generative Adversarial." Electronics 10, no. 13 (2021): 1525. http://dx.doi.org/10.3390/electronics10131525.

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Deep learning requires a large amount of datasets to train deep neural network models for specific tasks, and thus training of a new model is a very costly task. Research on transfer networks used to reduce training costs will be the next turning point in deep learning research. The use of source task models to help reduce the training costs of the target task models, especially heterogeneous systems, is a problem we are studying. In order to quickly obtain an excellent target task model driven by the source task model, we propose a novel transfer learning approach. The model linearly transfor

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Srebre, Matej, Pascal Schmolz, Hosein Hashemi, Martin Ritter, and Thomas Kuhr. "Generation of Belle II Pixel Detector Background Data with a GAN." EPJ Web of Conferences 245 (2020): 02010. http://dx.doi.org/10.1051/epjconf/202024502010.

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To match the statistical precision due to the large dataset that Belle II is expected to collect, simulations that accurately describe real data are required. The effect of beam background must be considered and can be taken into account with overlaying random trigger data, but its large size is a technical challenge. This problem can be mitigated by generating beam background data with generative adversarial networks. A proof of principle is shown for the background data recorded by the pixel vertex detector.

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I. Titov. "METHODOLOGICAL PRINCIPLES OF POSTNONCLASSICAL PSYCHOLOGY." Psychology and Personality, no. 1 (May 20, 2019): 9–40. http://dx.doi.org/10.33989/2226-4078.2019.1.163976.

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The transformation of psychological science at postnonclassical stage of its development demands theoretical and methodological reflection of the conceptual grounds of psychology. In this article, we have attempted to outline themost significant principles regarded to postnonclassicalpsychology such as 1) structural- genetic principle; 2) principle of subject (personal agency); 3) principle of orientation on cultural context; 4) principle of ontological constructivism; 5) principle of integration.Structural-genetic principlegrounds the view on psychological phenomenon as the unique and integra

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Sheppard, Daniel. "The International Criminal Court and "Internationally Recognized Human Rights": Understanding Article 21(3) of the Rome Statute." International Criminal Law Review 10, no. 1 (2010): 43–71. http://dx.doi.org/10.1163/157181209x12584562670811.

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AbstractArticle 21(3) of the Rome Statute requires that the law applied by the ICC be interpreted and applied in accordance with "internationally recognized human rights." Notwithstanding its paramountcy over other sources of law, Article 21(3) has yet to receive satisfactory consideration and analysis by the Court. In constructing a principled framework for how international human rights should operate within the applicable law of the Court, certain principles serve as important guideposts: rules of statutory interpretation, the complementarity principle, the structure of international human

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Carl, Michael. "Models of the Translation Process and the Free Energy Principle." Entropy 25, no. 6 (2023): 928. http://dx.doi.org/10.3390/e25060928.

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Translation process research (TPR) has generated a large number of models that aim at explaining human translation processes. In this paper, I suggest an extension of the monitor model to incorporate aspects of relevance theory (RT) and to adopt the free energy principle (FEP) as a generative model to elucidate translational behaviour. The FEP—and its corollary, active inference—provide a general, mathematical framework to explain how organisms resist entropic erosion so as to remain within their phenotypic bounds. It posits that organisms reduce the gap between their expectations and observat

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Zhang, Zhao, He Yan, Ping Wang, Richard Millham, and Renjie He. "Image Inpainting Method Based on Generative Adversary Networks." Journal of Internet Technology 25, no. 6 (2024): 945–53. http://dx.doi.org/10.70003/160792642024112506014.

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Aiming at the problems of missing and damaged image details, this paper proposes an image inpainting method that is preprocessed and then inpainted. First, through preprocessing that enhances and purifies the image, the clarity, saturation and contrast of the image are improved. Secondly, the image features are dynamically divided through the convolutional neural network, and the image is generated according to the principle of a generative adversarial network, finally, the adversarial strategy is used to promote the expression of the model, to make the repair result more realistic. Compared w

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Jiang, Jiacheng. "Study on the Attribution of Rights in Generative Artificial Intelligence Works." Journal of Intelligence and Knowledge Engineering 1, no. 2 (2023): 18–22. http://dx.doi.org/10.62517/jike.202304203.

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In modern life with ever-changing science and technology, artificial intelligence has become more and more popular, existing in all aspects of people's lives, playing an important and difficult to replace role. However, the lag of the law leads to the fact that it can not solve all disputes, especially in the copyright ownership of artificial intelligence works, whether it is infringement, contract disputes or copyright protection. It is an urgent problem to be solved. Because most of the disputes in the present stage of artificial intelligence are in the aspect of economic property, this pape

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Di Sciullo, Anna Maria. "Towards Reliable and Efficient Natural Language Processing in Emergent Technologies." Electronics 14, no. 15 (2025): 2922. https://doi.org/10.3390/electronics14152922.

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LLMs have created new possibilities for machines to learn from data and produce content without human intervention. We address the question of whether they provide reliable and efficient Natural Language Processing. We differentiate Generative Grammar Language Models from Large Language Models. We take Structure Dependency to be a first principle of language, a prerequisite to Semantic Compositionality, applying in conjunction with Principles of Efficient Computation to generate structured content. We discuss the behavior of ChatGPT-3.5, GPT-4.o mini and Grok 3 in response to complex queries i

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Besserve, Michel, Remy Sun, Dominik Janzing, and Bernhard Schölkopf. "A Theory of Independent Mechanisms for Extrapolation in Generative Models." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (2021): 6741–49. http://dx.doi.org/10.1609/aaai.v35i8.16833.

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Generative models can be trained to emulate complex empirical data, but are they useful to make predictions in the context of previously unobserved environments? An intuitive idea to promote such extrapolation capabilities is to have the architecture of such model reflect a causal graph of the true data generating process, such that one can intervene on each node independently of the others. However, the nodes of this graph are usually unobserved, leading to overparameterization and lack of identifiability of the causal structure. We develop a theoretical framework to address this challenging

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Zhou, Guoqiang, Yi Fan, Jiachen Shi, Yuyuan Lu, and Jun Shen. "Conditional Generative Adversarial Networks for Domain Transfer: A Survey." Applied Sciences 12, no. 16 (2022): 8350. http://dx.doi.org/10.3390/app12168350.

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Generative Adversarial Network (GAN), deemed as a powerful deep-learning-based silver bullet for intelligent data generation, has been widely used in multi-disciplines. Furthermore, conditional GAN (CGAN) introduces artificial control information on the basis of GAN, which is more practical for many specific fields, though it is mostly used in domain transfer. Researchers have proposed numerous methods to tackle diverse tasks by employing CGAN. It is now a timely and also critical point to review these achievements. We first give a brief introduction to the principle of CGAN, then focus on how

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Larson, Theodore, Fangyu Du, and Goutam Mylavarapu. "Proposal for organizational rules for new and prospective employees on the use of Generative tools." American Journal of Advanced research 8, no. 1 (2024): 7–10. https://doi.org/10.5281/zenodo.12211112.

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This paper proposes organizational guidelines for new and prospective employees on using generative tools like ChatGPT. It highlights the importance of setting clear expectations for these tools' utility, given their complex nature and the potential for misuse. The paper outlines a principle rule aiming for a targeted and fixed increase in worker productivity, alongside justification for setting minimum and maximum productivity targets. It discusses the necessity for a balanced approach to utilizing generative AI, emphasizing politeness in interactions, safeguarding private

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Jiang, Wei, Tianyi Yang, Ang Li, Yiyu Lin, and Xinzhu Bai. "The Application of Generative Artificial Intelligence in Virtual Financial Advisor and Capital Market Analysis." Academic Journal of Sociology and Management 2, no. 3 (2024): 40–46. https://doi.org/10.5281/zenodo.11112424.

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This paper explores the application of Generating Artificial Intelligence (GAI) to virtual financial advisory and capital market analysis. This paper first introduces the basic principle of GAI and its importance in financial decision-making, and then analyzes the problems and limitations of traditional financial advisory model through literature review and actual data. Then, it discusses the development trend and advantages of intelligent financial advisor, and the difference compared with traditional financial advisor model. Then, it describes the application of generative AI in the financia

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MAVROUDIS, Ioannis, Ioana –. Miruna BALMUS, and Alin CIOBICA. "Brain Function: Free Energy, Predictive Processing and Active Inference." Annals of the Academy of Romanian Scientists Series on Biological Sciences 12, no. 1 (2023): 108–10. http://dx.doi.org/10.56082/annalsarscibio.2023.1.108.

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"A potential new theory of brain function based on Bayesian inference could be that the brain is a predictive processing system that generates internal models of the world to make predictions about future sensory inputs. According to this theory, the brain generates internal models based on prior beliefs and past experiences, which are used to make predictions about future sensory inputs. In summary, the free energy principle focuses on minimizing the difference between the predicted and actual sensory inputs using a hierarchical generative model, while the predictive processing theory focuses

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Et. al., Dr Priyank Jain,. "Differentially Private Data Release: Bias Weight Perturbation Method - A Novel Approach." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 12, no. 10 (2021): 7165–73. http://dx.doi.org/10.17762/turcomat.v12i10.5607.

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Differential privacy plays the important role to preserve the individual data. In this research work, discussing a novel approach of releasing private data to the public, which is differentially private, called Bias Weight Perturbation Method. The approach follow here align with principle of differential privacy, it also used concept of statistical distance and statistical sample similarity to quantify the synthetic data generation loss, which is then used to validate our results. Our proposed approach make use of the deep generative models for providing privacy and it further produce syntheti

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Harker, Michael. "The Ethics of Argument: Rereading Kairos and Making Sense in a Timely Fashion." College Composition & Communication 59, no. 1 (2007): 77–97. http://dx.doi.org/10.58680/ccc20076381.

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This study challenges the prevailing interpretations of the Greek rhetorical principle of kairos “saying the right thing at the right time” and attempts to draw on a more nuanced understanding of the term in order to provide generative re-readings of three Braddock Award–winning essays.

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Scheiter, Matthias, Andrew Valentine, and Malcolm Sambridge. "Upscaling and downscaling Monte Carlo ensembles with generative models." Geophysical Journal International 230, no. 2 (2022): 916–31. http://dx.doi.org/10.1093/gji/ggac100.

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SUMMARY Monte Carlo methods are widespread in geophysics and have proved to be powerful in non-linear inverse problems. However, they are associated with significant practical challenges, including long calculation times, large output ensembles of Earth models, and difficulties in the appraisal of the results. This paper addresses some of these challenges using generative models, a family of tools that have recently attracted much attention in the machine learning literature. Generative models can, in principle, learn a probability distribution from a set of given samples and also provide a me

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