Relevant bibliographies by topics / Labelin

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Labelin'

Author: Grafiati
Published: 30 May 2022
Last updated: 31 July 2025

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Journal articles on the topic "Labelin"

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Suwilo, Saib, and Kristin Lamria. "Skolem Graceful Labelin on Broom Graph." Journal of Mathematics Technology and Education 3, no. 4 (2024): 392–400. https://doi.org/10.32734/jomte.v3i4.13375.

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Let G=(V,E) be a finite simple graph. Broom graph B_(m,n) consist of (m+n) nodes is a path graph P_m with m nodes and a star graph S_n with (n+1) nodes, which are aligned at 1 vertex is the endpoint of the path graph and the center point of the star graph. Skolem graceful labeling is two functions with the rule that the injective function f:V→{1,2,…,|V|} which induces a bijective function g:E→{1,2,…|E|}| such that g(e)=|f(u)-f(v)|, where u,v∈V, and e={u,v}∈E. In this research, broom graph admit skolem graceful labeling with m is odd and even.
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Ярош, Ольга, and Элла Митина. "Neuromarketing study of visual visibility of organic labelin." Маркетинг и маркетинговые исследования 2 (2019): 282–92. http://dx.doi.org/10.36627/2074-5095-2019-2-2-282-292.

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Ярош, Ольга, and Элла Митина. "Neuromarketing study of visual visibility of organic labelin." Маркетинг и маркетинговые исследования 4 (2019): 282–92. http://dx.doi.org/10.36627/2074-5095-2019-4-4-282-292.

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Al Fath, Muhammad Fajar, Salahuddin Salahuddin, and Muhammad Arhami. "Rancang Bangun Aplikasi Chatbot Untuk Deteksi Pecandu Pornografi Menggunakan Metode Support Vector Machine (SVM)." Journal of Artificial Intelligence and Software Engineering (J-AISE) 4, no. 1 (2024): 13. http://dx.doi.org/10.30811/jaise.v4i1.5399.

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Pornografi adalah representasi seksual dalam media yang menampilkan aktivitas seksual dan bahan yang menekankan pada unsur-unsur seksual yang dapat menyebabkan kecanduan. Salah satu faktor penyebab banyaknya pecandu pornografi di masyarakat karena kurangnya kesadaran dan pemahaman terhadap dampak negatif pornografi. Kesadaran diri akan membantu masyarakat terutama generasi muda untuk dapat meninggalkan konten pornografi dan terhindar dari dampaknya dalam jangka panjang. Penelitian ini bertujuan untuk memberikan solusi bagi para pecandu dan masyarakat untuk menghentikan kebiasaan menikmati porn
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Frank, Lawrence R., Eric C. Wong, and Richard B. Buxton. "QUANTITATIVE DYNAMIC IMAGING OF PERFUSION IN HUMAN SKELETAL MUSCLE DURING EXERCISE WITH ARTERIAL SPIN LABELIN." Medicine & Science in Sports & Exercise 31, Supplement (1999): S276. http://dx.doi.org/10.1097/00005768-199905001-01347.

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Wu, Xian, Wei Fan, and Yong Yu. "Sembler: Ensembling Crowd Sequential Labeling for Improved Quality." Proceedings of the AAAI Conference on Artificial Intelligence 26, no. 1 (2021): 1713–19. http://dx.doi.org/10.1609/aaai.v26i1.8351.

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Many natural language processing tasks, such as named entity recognition (NER), part of speech (POS) tagging, word segmentation, and etc., can be formulated as sequential data labeling problems. Building a sound labeler requires very large number of correctly labeled training examples, which may not always be possible. On the other hand, crowdsourcing provides an inexpensive yet efficient alternative to collect manual sequential labeling from non-experts. However the quality of crowd labeling cannot be guaranteed, and three kinds of errors are typical: (1) incorrect annotations due to lack of
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Barrientos, Christian. "On graphs with &#945;- and <em>b</em>-edge consecutive edge magic labelings." Indonesian Journal of Combinatorics 6, no. 1 (2022): 58. http://dx.doi.org/10.19184/ijc.2022.6.1.4.

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&lt;p&gt;Among the most studied graph labelings we have the varieties called alpha and edge-magic. Even when their definitions seem completely different, these labelings are related. A graceful labeling of a bipartite graph is called an α-labeling if the smaller labels are assigned to vertices of the same stable set. An edge-magic labeling of a graph of size &lt;em&gt;n&lt;/em&gt; is said to be &lt;em&gt;b&lt;/em&gt;-edge consecutive when its edges are labeled with the integers &lt;em&gt;b+1&lt;/em&gt;, &lt;em&gt;b+2&lt;/em&gt;, ..., &lt;em&gt;b+n&lt;/em&gt;, for some 0 ≤ &lt;em&gt;b&lt;/em&gt
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BANJADE, DEBENDRA, Menassie Ephrem, and Dashiell Stevanovich. "Finite Dimensional Labeled Graph Algebras." Journal of Mathematics and Statistics Studies 4, no. 3 (2023): 35–43. http://dx.doi.org/10.32996/jmss.2023.4.3.5.

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Given a directed graph E and a labeling L, one forms the labeled graph algebra by taking a weakly left-resolving labeled space (E, L, B) and considering a generating family of partial isometries and projections. In this paper, we discuss details in the formulation of the algebras, provide examples, and formulate a process that describes the algebra given the graph and a labelling.
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Rachmat, Antonius, and Yuan Lukito. "Implementasi Sistem Crowdsourced Labelling Berbasis Web dengan Metode Weighted Majority Voting." Jurnal ULTIMA InfoSys 6, no. 2 (2016): 76–82. http://dx.doi.org/10.31937/si.v6i2.223.

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Crowdsourced Labelling is a large scale data labelling process, solicits a large group of people to label the data, usually via Internet. This paper discusses about design and implementation of Web-based Crowdsourced Labelling. Supervised learning classification methods need labelled training data for its training phase. Unfortunately, in many cases, there aren’t any already available labelled training data. Large scale data labelling is a tedious and time consuming work. This research develops a web-based crowdsourced labelling which able to solicit a large group of people as data labeler to
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Early, Joseph, Eric Kelly, and Jesse Scearce. "LabelUp: Rapid Image Labeling." Proceedings of the AAAI Symposium Series 2, no. 1 (2024): 228–29. http://dx.doi.org/10.1609/aaaiss.v2i1.27677.

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MetroStar introduces "LabelUp," a transformative auto-labeling AI solution, custom-built for US government applications. Utilizing advanced transformer technology, LabelUp revolutionizes data labeling, significantly en-hancing operational AI models' efficiency for critical de-fense mechanisms. This innovative system promises over 800% workload reduction, facilitating rapid, precise la-beling with its intuitive low-code interface, featuring so-phisticated in-context-learning models. Compared to tra-ditional methods, LabelUp demonstrates staggering time and cost savings, redefining industry benc
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Dissertations / Theses on the topic "Labelin"

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Li, Xin. "Multi-label Learning under Different Labeling Scenarios." Diss., Temple University Libraries, 2015. http://cdm16002.contentdm.oclc.org/cdm/ref/collection/p245801coll10/id/350482.

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Computer and Information Science<br>Ph.D.<br>Traditional multi-class classification problems assume that each instance is associated with a single label from category set Y where |Y| > 2. Multi-label classification generalizes multi-class classification by allowing each instance to be associated with multiple labels from Y. In many real world data analysis problems, data objects can be assigned into multiple categories and hence produce multi-label classification problems. For example, an image for object categorization can be labeled as 'desk' and 'chair' simultaneously if it contains both ob
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Chen, Changsheng Verfasser], and Maximilian [Akademischer Betreuer] [Ulbrich. "Fluorescently labeled DNA probe in STORM imaging and single-molecule protein labeling." Freiburg : Universität, 2019. http://d-nb.info/1187658545/34.

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Chen, Changsheng [Verfasser], and Maximilian [Akademischer Betreuer] Ulbrich. "Fluorescently labeled DNA probe in STORM imaging and single-molecule protein labeling." Freiburg : Universität, 2019. http://d-nb.info/1187658545/34.

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Sardana, Malvika. "Development of New Late-Stage Labeling Methods with Labeled Carbon and Fluorine-18." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASF001.

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Le marquage isotopique est un outil précieux pour la découverte de nouveaux médicaments. Nous présentons dans cette thèse la conception et l’application de méthodes de marquage tardif de molécules bioactives avec du carbone et du fluor. Les réactions de carbonylation utilisant le monoxide de carbone sont connues pour être compatibles avec un marquage isotopique tardif des substances bioactives dans des conditions douces. La première partie de cette thèse décrit une réaction de photocarbonylation à partir d’iodures d’alkyles catalysée par le palladium sous lumière visible. Cette réaction polyva
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Pokora, John Michael. "The structural consequences of labelling DNA with nitroxide spin-label." Thesis, University of Dundee, 2010. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.527612.

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Shafer, Aaron Magers. "Site specific labeling of proteins in vivo and characterization of a spin labeled substance P agonist /." For electronic version search Digital dissertations database. Restricted to UC campuses. Access is free to UC campus dissertations, 2002. http://uclibs.org/PID/11984.

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Grankvist, Gunne. "Determinants of choice of eco-labeled products /." Göteborg, 2002. http://www-mat21.slu.se/publikation/pdf/Gunne.pdf.

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Kiesel, Kristin. "Consumer response to biotech food labeling: the effects of voluntary rBGH-free labels on fluid milk demand." Thesis, Montana State University, 2002. http://etd.lib.montana.edu/etd/2002/kiesel/KieselK2002.pdf.

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Consumer response to labeling policies for genetically modified organisms (GMO) in food products is investigated by considering voluntary labeling with respect to the use of the genetically modified Bovine Growth Horri:lone (rBGH) in fluid milk. Existing theoretical approaches on the provision and acquisition of information are extended to develop a framework that specifically addresses the effects of labeling on the relationship between consumer knowledge, information search and product choice. A new data set is utilized in the data analysis. The data set combines national-level supermarket s
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Pazuniak, Orest V. "Do Labels Make A Difference: Estimating The Impacts Of Vermont’s Gmo Labeling Law On Perceptions And Prices." ScholarWorks @ UVM, 2018. https://scholarworks.uvm.edu/graddis/974.

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Vermont is the first and only state in the US to establish mandatory labels for food containing genetically modified organisms (GMOs). This thesis investigates the impact of the mandatory labeling law as it relates to changes in prices, quantities sold, and opinions of GMOs. First, grocery store scanner data from Vermont and Oregon are compared using triple difference (difference-in-difference-in-difference) models. Next, Vermont, Oregon, and Colorado survey response data are compared using difference-in-difference models. The findings reveal that there is a general price premium for non-GMO g
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Buzzard, Julie A. "Patterns of Use Before and After a Labeling Intervention Among Collegiate Division I Athletes at a Fueling Station." DigitalCommons@USU, 2018. https://digitalcommons.usu.edu/etd/7334.

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The Fueling Station at Utah State University was created to provide pre-workout fueling and post-workout recovery foods to the Utah State University athletes. The athletes use an online survey to mark what foods they selected after each visit to the Fueling Station. There is a large amount of research on the lack of sports nutrition knowledge in collegiate athletes, but there is little information on the dietary intake of athletes, the education of athletes, and the education of macronutrient timing in athletes. A food labeling system named “Gain Your Edge” food labels that targeted the educat
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Books on the topic "Labelin"

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Hall, Ian H. Labels and labelling: A literature review. Pira International, 1994.

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Sangyōshō, Japan Keizai. Kankyō raberu shuhō no hyōjunka chōsa kenkyū: Seika hōkokusho : Heisei 13-nendo Keizai Sangyōshō itaku. Nihon Kikaku Kyōkai, 2002.

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McKelvey, Susan Richardson. A treasury of quilt labels. C & T Pub., 1993.

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Great Britain. Dept. of Trade and Industry., ed. Instructions for consumer products: Guidelines for better instructions and safety information for consumer products. H.M.S.O., 1988.

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Clabo, Margo J. The ultimate book of quilt labels. That Patchwork Place, 1998.

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McKelvey, Susan Richardson. A treasury of quilt labels. C & T Pub., 1993.

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Sangyōshō, Japan Keizai. Kankyō raberu shuhō no hyōjunka chōsa kenkyū: Seika hōkokusho : Heisei 13-nendo Keizai Sangyōshō itaku. Sangyō Kankyō Kanri Kyōkai, 2002.

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Causee, Linda. Foundation-pieced quilt labels: 30 designs. American School of Needlework, 2001.

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Thomas, Olejar, ed. Labels of distinction: Microbrewery label design. Madison Square Press, 1998.

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United States. Drug Enforcement Administration, ed. Project label: Alphabetical listings by drug labeler. U.S. Dept. of Justice, Drug Enforcement Administration, 1987.

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Book chapters on the topic "Labelin"

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Holkham, Tony. "Labels & Labelling." In Label Writing and Planning. Springer US, 1995. http://dx.doi.org/10.1007/978-1-4613-1231-4_26.

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Dai, Qionghai, and Yue Gao. "Typical Hypergraph Computation Tasks." In Artificial Intelligence: Foundations, Theory, and Algorithms. Springer Nature Singapore, 2023. http://dx.doi.org/10.1007/978-981-99-0185-2_5.

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AbstractAfter hypergraph structure generation for the data, the next step is how to conduct data analysis on the hypergraph. In this chapter, we introduce four typical hypergraph computation tasks, including label propagation, data clustering, imbalance learning, and link prediction. The first typical task is label propagation, which is to predict the labels for the vertices, i.e., assigning a label to each unlabeled vertex in the hypergraph, based on the labeled information. In general cases, label propagation is to propagate the label information from labeled vertices to unlabeled vertices t
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Antoni, G., T. Kihlberg, and B. Långström. "11C: Labeling Chemistry and Labeled Compounds." In Handbook of Nuclear Chemistry. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-0720-2_41.

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Ross, T. L., and H. J. Wester. "18F: Labeling Chemistry and Labeled Compounds." In Handbook of Nuclear Chemistry. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-0720-2_42.

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Alberto, R., and U. Abram. "99mTc: Labeling Chemistry and Labeled Compounds." In Handbook of Nuclear Chemistry. Springer US, 2011. http://dx.doi.org/10.1007/978-1-4419-0720-2_43.

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Kottke, Daniel, Christoph Sandrock, Georg Krempl, and Bernhard Sick. "A Stopping Criterion for Transductive Active Learning." In Machine Learning and Knowledge Discovery in Databases. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-26412-2_29.

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AbstractIn transductive active learning, the goal is to determine the correct labels for an unlabeled, known dataset. Therefore, we can either ask an oracle to provide the right label at some cost or use the prediction of a classifier which we train on the labels acquired so far. In contrast, the commonly used (inductive) active learning aims to select instances for labeling out of the unlabeled set to create a generalized classifier, which will be deployed on unknown data. This article formally defines the transductive setting and shows that it requires new solutions. Additionally, we formali
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Farrell, Michael. "Labelling: New Labels for Old?" In Investigating the Language of Special Education. Palgrave Macmillan UK, 2014. http://dx.doi.org/10.1057/9781137434715_5.

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Huang, Zhi-Dong, Sheung-Hung Poon, and Chun-Cheng Lin. "Boundary Labeling with Flexible Label Positions." In Algorithms and Computation. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-04657-0_7.

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Eaglstein, William H. "FDA, Labels and Labeling for Medical Products." In The FDA for Doctors. Springer International Publishing, 2014. http://dx.doi.org/10.1007/978-3-319-08362-9_14.

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Babka, Martin, Jan Bulánek, Vladimír Čunát, Michal Koucký, and Michael Saks. "On Online Labeling with Polynomially Many Labels." In Algorithms – ESA 2012. Springer Berlin Heidelberg, 2012. http://dx.doi.org/10.1007/978-3-642-33090-2_12.

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Conference papers on the topic "Labelin"

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Ferrero, Vincenzo J., Arvind Shankar Raman, Bryony DuPont, and Karl R. Haapala. "Understanding the Sustainability of Eco-Labeled Products When Compared to Conventional Alternatives." In ASME 2017 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2017. http://dx.doi.org/10.1115/detc2017-68339.

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Sustainability considerations are becoming an intrinsic part of product design and manufacturing. Today’s consumers rely on package labeling to relay useful information about the environmental impact of a given product. As such, eco-labeling has become an important influence on how consumers interpret the sustainability of products. Three categories of eco-labels are theorized: Type I focuses on the use of labels that are certified by a reputable third party. Type II are eco-labels that are self-declared, potentially lacking scientific merit. Type III eco-labeling indicates the public availabi
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Huang, Sheng-Jun, Jia-Lve Chen, Xin Mu, and Zhi-Hua Zhou. "Cost-Effective Active Learning from Diverse Labelers." In Twenty-Sixth International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/261.

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In traditional active learning, there is only one labeler that always returns the ground truth of queried labels. However, in many applications, multiple labelers are available to offer diverse qualities of labeling with different costs. In this paper, we perform active selection on both instances and labelers, aiming to improve the classification model most with the lowest cost. While the cost of a labeler is proportional to its overall labeling quality, we also observe that different labelers usually have diverse expertise, and thus it is likely that labelers with a low overall quality can p
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Da Silva, Thiago Gouveia. "The Minimum Labeling Spanning Tree and Related Problems." In XXXII Concurso de Teses e Dissertações da SBC. Sociedade Brasileira de Computação - SBC, 2019. http://dx.doi.org/10.5753/ctd.2019.6333.

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The minimum labeling spanning tree problem (MLSTP) is a combinatorial optimization problem that consists in finding a spanning tree in a simple edge-labeled graph, i.e., a graph in which each edge has one label associated, by using a minimum number of labels. It is an NP-hard problem that has attracted substantial research attention in recent years. In its turn, the generalized minimum labeling spanning tree problem (GMLSTP) is a generalization of the MLSTP that allows the situation in which multiple labels can be assigned to an edge. Both problems have several practical applications in import
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Gao, Yi, Miao Xu, and Min-Ling Zhang. "Unbiased Risk Estimator to Multi-Labeled Complementary Label Learning." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/415.

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Multi-label learning (MLL) usually requires assigning multiple relevant labels to each instance. While a fully supervised MLL dataset needs a large amount of labeling effort, using complementary labels can help alleviate this burden. However, current approaches to learning from complementary labels are mainly designed for multi-class learning and assume that each instance has a single relevant label. This means that these approaches cannot be easily applied to MLL when only complementary labels are provided, where the number of relevant labels is unknown and can vary across instances. In this
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Ding, Zhuojun, Wei Wei, Xiaoye Qu, and Dangyang Chen. "Improving Pseudo Labels with Global-Local Denoising Framework for Cross-lingual Named Entity Recognition." 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.2024/691.

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Cross-lingual named entity recognition (NER) aims to train an NER model for the target language leveraging only labeled source language data and unlabeled target language data. Prior approaches either perform label projection on translated source language data or employ a source model to assign pseudo labels for target language data and train a target model on these pseudo-labeled data to generalize to the target language. However, these automatic labeling procedures inevitably introduce noisy labels, thus leading to a performance drop. In this paper, we propose a Global-Local Denoising framew
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Park, Seyoung, Yilan Jiang, and Harrison Kim. "Automated Sub-Feature Labeling Using Prompt-Based Pretrained Language Model." In ASME 2024 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference. American Society of Mechanical Engineers, 2024. http://dx.doi.org/10.1115/detc2024-142891.

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Abstract Many studies have been utilizing online user-generated data to draw product design implications via supervised and unsupervised approaches. While the supervised learning methods typically yield higher performance, they demand extensive data labeling tasks, consuming significant time and effort. This study proposes a framework that automatically labels online user data to address this limitation. The proposed framework consists of two pseudo-labeling mechanisms, keyword detection and prompting Pretraied Language Model (PLM). The first stage defines keywords for the target topic and the
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Wang, Kuo, Yuxiang Nie, Chaowei Fang, et al. "Double-Check Soft Teacher for Semi-Supervised Object Detection." 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.2022/199.

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In the semi-supervised object detection task, due to the scarcity of labeled data and the diversity and complexity of objects to be detected, the quality of pseudo-labels generated by existing methods for unlabeled data is relatively low, which severely restricts the performance of semi-supervised object detection. In this paper, we revisit the pseudo-labeling based Teacher-Student mutual learning framework for semi-supervised object detection and identify that the inconsistency of the location and feature of the candidate object proposals between the Teacher and the Student branches are the f
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Peng, Xiaomeng, Xiaoning Jin, Shiming Duan, and Chaitanya Sankavaram. "Class Incremental Fault Detection and Diagnosis With Unlabeled Condition Monitoring Data." In ASME 2020 15th International Manufacturing Science and Engineering Conference. American Society of Mechanical Engineers, 2020. http://dx.doi.org/10.1115/msec2020-8431.

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Abstract Data-driven methods for fault detection and diagnostics (FDD) require a large amount of labeled data and knowledge about complete failure modes set to train a reliable classifier as well as require the same label space in an online testing phase. Typical supervised classifiers in FDD can only predict precedented faults, limiting their performance in identifying unprecedented failure modes in on-line testing data. In addition, in most applications, it may be expensive and time-consuming to obtain sufficient labeled samples. This study focuses on fault detection and diagnosis without su
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Gao, Li, Hong Yang, Chuan Zhou, Jia Wu, Shirui Pan, and Yue Hu. "Active Discriminative Network Representation Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/296.

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Most of current network representation models are learned in unsupervised fashions, which usually lack the capability of discrimination when applied to network analysis tasks, such as node classification. It is worth noting that label information is valuable for learning the discriminative network representations. However, labels of all training nodes are always difficult or expensive to obtain and manually labeling all nodes for training is inapplicable. Different sets of labeled nodes for model learning lead to different network representation results. In this paper, we propose a novel metho
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Feng, Lei, and Bo An. "Leveraging Latent Label Distributions for Partial Label Learning." In Twenty-Seventh International Joint Conference on Artificial Intelligence {IJCAI-18}. International Joint Conferences on Artificial Intelligence Organization, 2018. http://dx.doi.org/10.24963/ijcai.2018/291.

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In partial label learning, each training example is assigned a set of candidate labels, only one of which is the ground-truth label. Existing partial label learning frameworks either assume each candidate label of equal confidence or consider the ground-truth label as a latent variable hidden in the indiscriminate candidate label set, while the different labeling confidence levels of the candidate labels are regrettably ignored. In this paper, we formalize the different labeling confidence levels as the latent label distributions, and propose a novel unified framework to estimate the latent la
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Reports on the topic "Labelin"

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Kuchler, Fred, Megan Sweitzer, and Carolyn Chelius. prevalence of the "natural" claim on food product packaging. USDA Economic Research Service, 2023. http://dx.doi.org/10.32747/2023.8023700.ers.

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U.S. food suppliers make claims about their production processes on food packaging that highlight attributes some consumers want while charging a higher price than for unlabeled products. Some labels use such claims as "USDA Organic" and "raised without antibiotics," which require different and more expensive production techniques than conventional agriculture. However, food suppliers can use the label that claims the food is "natural" at a relatively low cost because regulatory agencies treat the claim as meaning nothing artificial was added and the product was minimally processed. Numerous c
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Yang, KyoungLang, and Gunda I. Georg. Synthesis of Cryptophycin Affinity Labels and Tubulin Labeling. Defense Technical Information Center, 2005. http://dx.doi.org/10.21236/ada443679.

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Yang, Kyounglang, and AGunda I. Georg. Synthesis of Cryptophycin Affinity Labels and Tubulin Labeling. Defense Technical Information Center, 2004. http://dx.doi.org/10.21236/ada432471.

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Ramadas, Vidya. Synthesis of Cryptophycin Affinity Labels and Tubulin Labeling. Defense Technical Information Center, 2003. http://dx.doi.org/10.21236/ada416994.

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Yang, KyoungLang, and Gunda I. Georg. Synthesis of Cryptophycin Affinity Labels and Tubulin Labeling. Defense Technical Information Center, 2006. http://dx.doi.org/10.21236/ada474734.

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Montgomery, Doug, Mark Carson, Timothy Winters, Michayla Newcombe, and Timothy Carlin. NIST IPv6 Profile. National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.sp.500-267ar1.

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This profile establishes a basic taxonomy of IPv6 capabilities, defined in terms of IETF specifications, resulting in specific capability labels for common network functions and usage scenarios. The profile maps each such labeled capability to one or more specific technical specifications, or parts of specifications. Each labeled capability adopts by reference the normative requirements of the cited specifications. In rare cases the profile may augment or modify the normative requirements of a base specification. The defined capability labels effectively form a vocabulary for expressing IPv6 r
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Montgomery, Doug, Mark Carson, Timothy Winters, Michayla Newcombe, and Timothy Carlin. USGv6 Profile. National Institute of Standards and Technology, 2020. http://dx.doi.org/10.6028/nist.sp.500-267br1.

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This profile establishes a basic taxonomy of IPv6 capabilities, defined in terms of IETF specifications, resulting in specific capability labels for common network functions and usage scenarios. The profile maps each such labeled capability to one or more specific technical specifications, or parts of specifications. Each labeled capability adopts by reference the normative requirements of the cited specifications. In rare cases the profile may augment or modify the normative requirements of a base specification. The defined capability labels effectively form a vocabulary for expressing IPv6 r
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Heidelmeier, Lisa Isabel, and Stefanie Y. Schmitt. Awards vs. Labels : Incentivizing Investments in Environmental Quality. Otto-Friedrich-Universität Bamberg, 2025. https://doi.org/10.20378/irb-108375.

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Although consumers often care about environmental quality, limited attention impairs consumers’ perception of environmental quality. Environmental awards and labels make environmental quality salient and attract consumers’ attention. We analyze how awards and labels affect firms’ investments in environmental quality and social welfare. We show that, with an award, both firms invest in environmental quality; with a label, only one firm invests. Under awards, investments depend positively on salience. Under labels, investments depend non-monotonically on salience. A welfare-maximizing social pla
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Otani, T., and D. Li, eds. Generalized Labels for Lambda-Switch-Capable (LSC) Label Switching Routers. RFC Editor, 2011. http://dx.doi.org/10.17487/rfc6205.

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Freytag, A. Guidance on Designing Label Generation Rulesets (LGRs) Supporting Variant Labels. RFC Editor, 2017. http://dx.doi.org/10.17487/rfc8228.

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