Добірка наукової літератури з теми "Weight labels"
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Статті в журналах з теми "Weight labels":
Wang, Zhe, Hao Xu, Pan Zhou, and Gang Xiao. "An Improved Multilabel k-Nearest Neighbor Algorithm Based on Value and Weight." Computation 11, no. 2 (February 13, 2023): 32. http://dx.doi.org/10.3390/computation11020032.
Haunert, Jan-Henrik, and Alexander Wolff. "BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 109–14. http://dx.doi.org/10.5194/isprs-archives-xli-b2-109-2016.
Haunert, Jan-Henrik, and Alexander Wolff. "BEYOND MAXIMUM INDEPENDENT SET: AN EXTENDED MODEL FOR POINT-FEATURE LABEL PLACEMENT." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLI-B2 (June 7, 2016): 109–14. http://dx.doi.org/10.5194/isprsarchives-xli-b2-109-2016.
Wang, Wanzhu, and Yong Liu. "Multi-label Feature Selection based on Label-specific features and Manifold Learning." Academic Journal of Science and Technology 10, no. 1 (March 27, 2024): 364–69. http://dx.doi.org/10.54097/astymd16.
Zhang, Yaojie, Huahu Xu, Junsheng Xiao, and Minjie Bian. "JoSDW: Combating Noisy Labels by Dynamic Weight." Future Internet 14, no. 2 (February 2, 2022): 50. http://dx.doi.org/10.3390/fi14020050.
A. S., Saranya, and Santhosh Kumar K. R. "On the total edge irregularity strength of certain classes of cycle related graphs." Proyecciones (Antofagasta) 43, no. 1 (March 20, 2024): 53–67. http://dx.doi.org/10.22199/issn.0717-6279-5728.
Essayli, Jamal H., Jessica M. Murakami, Rebecca E. Wilson, and Janet D. Latner. "The Impact of Weight Labels on Body Image, Internalized Weight Stigma, Affect, Perceived Health, and Intended Weight Loss Behaviors in Normal-Weight and Overweight College Women." American Journal of Health Promotion 31, no. 6 (August 23, 2016): 484–90. http://dx.doi.org/10.1177/0890117116661982.
Karaca, Adeviyye, Kamil Can Akyol, Mustafa Keşaplı, Faruk Güngör, Umut Cengiz Çakır, Angelika Janitzky, and Ramazan Güven. "Do Clothing Labels Play a Role for Weight Estimation in Pediatric Emergencies? A Prospective, Cross-Sectional Study." Prehospital and Disaster Medicine 36, no. 3 (February 26, 2021): 295–300. http://dx.doi.org/10.1017/s1049023x21000194.
Liu, Jinghua, Songwei Yang, Hongbo Zhang, Zhenzhen Sun, and Jixiang Du. "Online Multi-Label Streaming Feature Selection Based on Label Group Correlation and Feature Interaction." Entropy 25, no. 7 (July 17, 2023): 1071. http://dx.doi.org/10.3390/e25071071.
O’Connor, Alan. "Habitus and field: Punk record labels in Spain." Punk & Post Punk 10, no. 2 (June 1, 2021): 265–89. http://dx.doi.org/10.1386/punk_00071_1.
Дисертації з теми "Weight labels":
Chazelle, Thomas. "Influence sociale sur la représentation corporelle : Approche expérimentale de l'effet des médias et des labels de poids sur des jugements de corpulence." Electronic Thesis or Diss., Université Grenoble Alpes, 2023. http://www.theses.fr/2023GRALS063.
Body representation is the set of cognitive functions that track the state of the body. It is involved in a variety of situations, such as the perception of the physical dimensions of the body, action, and the generation of attitudes towards the body. To perform these functions, it relies on the flexible use of a range of sensorimotor information, as well as on the individual's beliefs, expectations and emotions. Among the sources of information available about the body, social influence can be a risk, maintenance, and severity factor in body image distortions. However, while social influence on the attitudinal aspects of body representation is well established, there is little experimental evidence of such influence on its perceptual aspects. The aim of this thesis is to study the integration of social information into the perceptual dimension of the representation of body size. To this end, we conducted a series of experiments with young women, a demographic that is particularly prone to distortions of body representation. A first axis focuses on interpersonal influence by testing the effect of weight labels on perceptual judgments. To investigate their informational influence, we manipulated the reliability of multiple cues to study how they were combined. Our results indicate that weight labels have a limited influence on judgments of body size. A second axis focuses on another type of social influence, media influence. Visual overexposure to specific body types is associated with body dissatisfaction, and could help explain the perceptual and attitudinal distortions of body representation. In this context, visual adaptation to bodies could explain how prolonged exposure to thin bodies can lead to an overestimation of one's own body size. We tested some of the hypotheses of this adaptation theory of body image distortion. These experiments highlight some limitations of the adaptation account; in particular, it is uncertain whether adaptation effects can influence the representation that individuals have of their own bodies. In conclusion, our results suggest that the perceptual dimension of the representation of body size may be resistant to some types of interpersonal and media social influence
Araújo, Olegário da Cruz de. "In-store attractiveness of national brands and private labels in an emerging market." reponame:Repositório Institucional do FGV, 2018. http://hdl.handle.net/10438/20705.
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Emerging markets are considered relevant for international manufacturers and retailers to grow their turnovers. In order to achieve their goals, manufacturers and retailers are executing different initiatives to attract new customers such as in-store promotions. However, both in the US and here in Brazil, the results of these actions are questioned. Retailers are also investing in their Private Labels (PLs), which can alter the competitive dynamics within the categories. In the United States and Europe, studies were conducted to assess in-store promotions, impulses and responses in short-term and long-term sales for National Brands (NBs) and also Private Labels (PLs). The research question of this study was to evaluate if in Brazil, an emerging market, the attractiveness of Weighted Distribution, Price and Promotions of National Brands and Private Labels provide similar responses to the impulses. In order to evaluate if the impulses provide long-term residual effects for National Brands (NBs) and Private Labels (PLs), Vectors of Auto Regression (VAR) model was used in a continuous panel of self-service food stores in Greater São Paulo, which is the main metropolitan region of Brazil. The databases by categories (powdered coffee, biscuit, and ready-to-serve fruit juice) contained information of 25 months (November 2013 to November 2015) for each variable (Weighted Distribution, Price and Promotions), by NBs and PLs. The result of this study points out that there is a difference in responses to the impulses (distribution, price, and promotions) between NBs and PLs. National Brands (NBs) showed a greater number of situations with positive residual effects on long-term sales. However, the long-term response on sales occurred only for less than the half of the total potential situations. In other words, more than half of the total potential situations give an absence of statistical significance. The study indicates that there are retailers developing differentiated actions with Private Labels and obtaining, in their sales, positive long-term residual effects. Although modestly, this study contributes to the retail literature by using an econometric model (VAR) to analyze the impulse in some in-store attractiveness variables their long-term sales response to NBs and PLs in an emerging market. In short, the main contribution from the observations of the analyzed categories is that it is possible to Private Label compete without price sensibility and also positioning PL above the average price of the category/segment. The results also suggest that there is an opportunity to review the modus operandi of in-store promotion to get better results.
Mercados emergentes são importantes para as receitas totais de fabricantes e varejistas internacionais. Estudos de companhias globais de pesquisa, que atuam no Brasil, apontam que os investimentos em ações promocionais no ponto-de-venda, pelas Marcas de Fabricantes, aumentaram, mas há questionamentos quanto ao retorno destas iniciativas. Os varejistas também têm investido em Marcas Próprias. Nos Estados Unidos e Europa há vários estudos sobre o estímulos dentro do ponto-de-venda para as Marcas dos Fabricantes e Marcas Próprias e o impacto nas vendas no curto e longo prazo. O objetivo central deste estudo é avaliar se, em um mercado emergente, o nivel de atratividade das ações realizadas pelas Marcas de Fabricantes e Marcas Próprias dentro das lojas proporcionam respostas similares de curto e longo prazo aos impulsos realizados. Para analisar os efeitos destes impulsos foi utilizado o modelo de Vetores de Auto Regressão (VAR) em um painel continuo de lojas de autosserviço alimentar, na principal região metropolitana do Brasil, a Grande São Paulo. As bases de dados por categoria (Café em Pó, Biscoito e Suco Pronto para Consumo), continham informações de 25 meses (novembro de 2013 à novembro de 2015), com dados de distribuição ponderada, preço e promoções, O resultado deste estudo aponta que há diferenças entre Marcas de Fabricantes e Marcas Próprias nas respostas de longo prazo aos estímulos promocionais. Embora as Marcas de Fabricantes tenham apresentado um maior número de situações com efeitos residuais positivos nas vendas de longo prazo do que as Marcas Próprias, apenas menos da metade das situações apresentaram resultados de longo prazo. O estudo também sinaliza que há varejistas desenvolvendo ações diferenciadas com Marcas Próprias e obtendo, em suas vendas, efeitos residuais positivos de longo prazo, na mesma intensidade das Marcas de Fabricantes. Embora de forma modesta, esta pesquisa contribui para a literatura ao utilizar um modelo econométrico (VAR) para analisar os impulsos aplicados em distribuição, preço e promoção das Marcas dos Fabricantes e das Marcas Próprias em um mercado emergente. A principal contribuição deste estudo, a partir das categorias analisadas, é que a Marca Própria, não necessariamente, precisa atuar apenas com um posicionamento de preço baixo e/ou reduzir preços para competir dentro da categoria ou segmento no qual está inserida. Além disto, o estudo também sugere que as há espaço para rever as práticas promocionais ou até operacionais, considerando o baixo retorno proporcionado para Marcas de Fabricantes e Marcas Próprias.
Simmons, Mark R. "Comparison of Weight Loss Outcome Measures in Adolescent Bariatric Surgery Patients using Growth Curve Modeling." University of Cincinnati / OhioLINK, 2015. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1447689830.
Baier, Moritz C. [Verfasser]. "Living Polymerization to Ultra-High Molecular Weight and Dye-Labeled Polyethylene for Single-Molecule Fluorescence Microscopy and Reactor Blends / Moritz C. Baier." Konstanz : Bibliothek der Universität Konstanz, 2016. http://d-nb.info/1173616454/34.
Sarinnapakorn, Kanoksri. "Induction of Classifiers from Multi-labeled Examples: an Information-retrieval Point of View." Scholarly Repository, 2007. http://scholarlyrepository.miami.edu/oa_dissertations/16.
Mohammed, Kader Hamno. "Development of a label-free biosensor method for the identification of sticky compounds which disturb GPCR-assays." Thesis, Uppsala universitet, Institutionen för biologisk grundutbildning, 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-220645.
Mata, Jutta. "Healthy food choice." Doctoral thesis, Humboldt-Universität zu Berlin, Mathematisch-Naturwissenschaftliche Fakultät II, 2008. http://dx.doi.org/10.18452/15723.
This dissertation focuses on food-related decision making, in particular, how food related environments and cognition interact to determine people’s food choices. The first manuscript, “When Diets Last: Lower Cognitive Complexity Increases Diet Adherence,” investigates the role of the cognitive complexity in diet adherence. Can weight loss diets fail because they are too complicated from a cognitive point of view, meaning that dieters are not able to recall or process the diet rules? The impact of excessive cognitive demands on diet adherence were investigated with 1,136 dieters in a longitudinal online-questionnaire. We measured perceived rule complexity controlling for other factors known to influence adherence. Previous diet behavior, self-efficacy, planning and perceived rule complexity predicted an increased risk to quit the diet prematurely, with self-efficacy and diet complexity being the strongest factors. The second manuscript, “Meat Label Design: Effects on Stage Progression, Risk Perception, and Product Evaluation,” presents two studies which tested the impact of health-related meat labels on product evaluation and intention. Specifically, the studies examined how informational content and the context (separate vs. conjoint evaluation) in which labels are assessed influence the evaluation of meat products. The results showed that conjoint assessment of labels can lead to contrary product rankings compared to separate evaluations. Moreover, the results suggest that being exposed to food labels containing specific health-relevant information can increase motivation to consider health aspects in those consumers without previous intention to do so. The third manuscript, “Predicting Children’s Meal Preferences: How Much Do Parents Know?” investigated prediction behavior concerning other people’s food choices. In particular, it asked how accurately and what cues parents use to predict their children’s meal choices. Overall, parents’ prediction accuracy matched the stability of children’s meal choices, implying that accuracy was as high as can be expected. The results suggest parents were able to obtain high predictive accuracy by using specific knowledge about their child’s likes and projecting their own preferences.
McKinnon, Loretta Carmen. "The contribution of psychosocial factors to socioeconomic differences in food purchasing." Thesis, Queensland University of Technology, 2012. https://eprints.qut.edu.au/60893/1/Loretta_McKinnon_Thesis.pdf.
Villars, Clément. "Mesure objective de l’activité physique en conditions de vie libre et relations avec l’adiponectine." Thesis, Lyon 1, 2011. http://www.theses.fr/2011LYO10301/document.
Accurate measurements of physical activity in free living are needed to establish what dose of physical activity is necessary for obtaining a specific health benefits. The first aim of this work was to validate the Actiheart (which combines heart rate and accelerometry sensors) with doubly labeled water (DLW). We show a good level of concordance between physical activity energy expenditure (PAEE) estimated by Actiheart and DLW. Individualization of the relationship between heart rate and PAEE by an incremental test is needed for an accurate estimate of the PAEE at the individual level and to evaluate changes induced by an intervention. In laboratory, we show that the accuracy of Actiheart is activitydependent. This requires the establishment of their recognition from new sensors and mathematical models. Adiponectin, hormone secreted by adipose tissue, has a role in energy metabolism and its secretion decreases with obesity. However the effects of physical activity remain in contradiction in published studies. The second objective of this work was to evaluate the effect of physical activity and intervention with weight control on plasma adiponectin. We show that the total and high molecular weight adiponectin were negatively associated with modifications of the physical activity level. Further work is however necessary to understand the mechanisms underlying this modulation of plasma adiponectin which does not seem related to changes in synthesis in adipose tissue or muscle
Chen, Kuan-Tzu, and 陳冠慈. "The Impact of Reading Food Nutrition Labels for Weight Control on Working Adults’Body Mass Index." Thesis, 2012. http://ndltd.ncl.edu.tw/handle/70805620037973064829.
國立臺灣大學
健康政策與管理研究所
101
Overweight and obesity is the world''s fifth largest cause of death risk, WHO also estimated that there are at least 2.8 million adults died of overweight or obese. According to the Department of Health, Executive Yuan, R.O.C (Taiwan), "2005-2008 National Nutrition and Health Survey" results of adult overweight and obesity rate of 44.1%, 50.8% were male. Women accounted for 36.9%. 2008 survey by the Ministry of Education student height and weight data show that one in four children are overweight or obese. Overweight and obese has become a health problem. Most of the overweight or obese are cause from diet and physical activity in global, so we focus on the effect of nutrition label using on Body Mass Index BMI. In order to provide obesity prevention and treatment plan at the future. Using 2005 National Health Interview Survey (NHIS), 18 to 64 year-old samples, 4585 female, 6219 male, include basic personal information, self-report height and weight, health behavior, the economic situation, and the health behavior in the food nutrition label reading. We use Propensity Score Matching (PSM) to control other variables. Results showed that the factor of reading food nutrition labels in gender is slightly different. The using of nutrition labels is relative to higher social-economic status, healthy eating behavior, less high-calorie and high sugar eating patterns. The food nutrition using is a part of health behavior, but we found that the user have higher BMI than the other. Government agencies should pay attention to people recommended nutrient intake for cognitive education and related policy implementation, as a health promote information.
Книги з теми "Weight labels":
Brazil. Serviço Nacional de Aprendizagem Industrial. Departamento Nacional. Coletânea de portarias de produtos pré-medidos. Brasília: CNI SENAI, 2000.
Day, Gloria. Weight of Labels: A Poetic Display of an Internal Shift. Lulu Press, Inc., 2018.
Radman-Livaja, Ivan. Prices and Costs in the Textile Industry in the Light of the Lead Tags from Siscia. Oxford University Press, 2017. http://dx.doi.org/10.1093/oso/9780198790662.003.0013.
Young, Deborah E. Swatch Reference Guide for Fashion Fabrics: 5th Edition. 5th ed. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9781501373206.
Zoumbaris, Sharon K., and Marjolijn Bijlefeld. Food and You. Greenwood, 2001. http://dx.doi.org/10.5040/9798400652301.
Kirchman, David L. Degradation of organic matter. Oxford University Press, 2018. http://dx.doi.org/10.1093/oso/9780198789406.003.0007.
Millan, John. Coins Weights & Measures, Ancient & Modern, of All Nations. Reduced Into English on Above 100 Tables, Collected & Methodiz'd from Newton, Folkes, ... of Specific Gravities, by Newton, Labelye. Gale Ecco, Print Editions, 2018.
Sarkar, Ajoy K., Ingrid Johnson, and Allen C. Cohen. J.J. Pizzuto’s Fabric Science Swatch Kit: 12th Edition. 12th ed. Bloomsbury Publishing Inc, 2023. http://dx.doi.org/10.5040/9781501367892.
Частини книг з теми "Weight labels":
Shield, Andrew DJ. "“White is a color, Middle Eastern is not a color”: Drop-Down Menus, Racial Identification, and the Weight of Labels." In Immigrants on Grindr, 185–225. Cham: Springer International Publishing, 2019. http://dx.doi.org/10.1007/978-3-030-30394-5_6.
Xu, Jianhua. "Multi-Label Weighted k-Nearest Neighbor Classifier with Adaptive Weight Estimation." In Neural Information Processing, 79–88. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-24958-7_10.
Pirouz, Matin. "Balanced Weighted Label Propagation." In Lecture Notes in Networks and Systems, 1–12. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80126-7_1.
Wang, Hongzhi, Jung Wook Suh, John Pluta, Murat Altinay, and Paul Yushkevich. "Optimal Weights for Multi-atlas Label Fusion." In Lecture Notes in Computer Science, 73–84. Berlin, Heidelberg: Springer Berlin Heidelberg, 2011. http://dx.doi.org/10.1007/978-3-642-22092-0_7.
Yang, Lei, Zhan Shi, Dan Feng, Wenxin Yang, Jiaofeng Fang, Shuo Chen, and Fang Wang. "MLND: A Weight-Adapting Method for Multi-label Classification Based on Neighbor Label Distribution." In Web and Big Data, 639–54. Cham: Springer International Publishing, 2020. http://dx.doi.org/10.1007/978-3-030-60259-8_47.
Verma, Gurudatta, and Tirath Prasad Sahu. "Exploring Label-Specific Feature Weights for Multi-label Feature Selection Using FWMABAC-MFS." In Proceedings of 4th International Conference on Frontiers in Computing and Systems, 321–35. Singapore: Springer Nature Singapore, 2024. http://dx.doi.org/10.1007/978-981-97-2611-0_22.
Sun, Chong, Weiyu Zhou, Zhongshan Song, Fan Yin, Lei Zhang, and Jianquan Bi. "Weighted Multi-label Learning with Rank Preservation." In Big Data, 312–24. Singapore: Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-15-1899-7_22.
Mohan, Anshuman, Wei Xiang Leow, and Aquinas Hobor. "Functional Correctness of C Implementations of Dijkstra’s, Kruskal’s, and Prim’s Algorithms." In Computer Aided Verification, 801–26. Cham: Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-81688-9_37.
Wang, Xin, Songlei Jian, Kai Lu, and Xiaoping Wang. "Unified Weighted Label Propagation Algorithm Using Connection Factor." In Advanced Data Mining and Applications, 434–44. Cham: Springer International Publishing, 2016. http://dx.doi.org/10.1007/978-3-319-49586-6_29.
Wang, Lulu, Hong Shen, and Hui Tian. "Weighted Ensemble Classification of Multi-label Data Streams." In Advances in Knowledge Discovery and Data Mining, 551–62. Cham: Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-57529-2_43.
Тези доповідей конференцій з теми "Weight labels":
Feng, Lei, Senlin Shu, Zhuoyi Lin, Fengmao Lv, Li Li, and Bo An. "Can Cross Entropy Loss Be Robust to Label Noise?" In Twenty-Ninth International Joint Conference on Artificial Intelligence and Seventeenth Pacific Rim International Conference on Artificial Intelligence {IJCAI-PRICAI-20}. California: International Joint Conferences on Artificial Intelligence Organization, 2020. http://dx.doi.org/10.24963/ijcai.2020/305.
Yang, Xu, Yanan Gu, Kun Wei, and Cheng Deng. "Exploring Safety Supervision for Continual Test-time Domain Adaptation." In Thirty-Second International Joint Conference on Artificial Intelligence {IJCAI-23}. California: International Joint Conferences on Artificial Intelligence Organization, 2023. http://dx.doi.org/10.24963/ijcai.2023/183.
Wu, Junshuang, Richong Zhang, Yongyi Mao, Hongyu Guo, and Jinpeng Huai. "Modeling Noisy Hierarchical Types in Fine-Grained Entity Typing: A Content-Based Weighting Approach." In Twenty-Eighth International Joint Conference on Artificial Intelligence {IJCAI-19}. California: International Joint Conferences on Artificial Intelligence Organization, 2019. http://dx.doi.org/10.24963/ijcai.2019/731.
Xiong, Feng, Jiayi Tian, Zhihui Hao, Yulin He, and Xiaofeng Ren. "SCMT: Self-Correction Mean Teacher for Semi-supervised Object Detection." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/207.
Khangura, Jasan, Melanie Flores, and Jane Ishmael. "Product text labels indicate the presence of other pharmacologically active ingredients in many OTC hemp- and CBD-containing preparations." In 2021 Virtual Scientific Meeting of the Research Society on Marijuana. Research Society on Marijuana, 2022. http://dx.doi.org/10.26828/cannabis.2022.01.000.32.
Zhang, Qian-Wen, Ximing Zhang, Zhao Yan, Ruifang Liu, Yunbo Cao, and Min-Ling Zhang. "Correlation-Guided Representation for Multi-Label Text Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/463.
Wu, Yanan, He Liu, Songhe Feng, Yi Jin, Gengyu Lyu, and Zizhang Wu. "GM-MLIC: Graph Matching based Multi-Label Image Classification." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/163.
Xia, Shi-Yu, Jiaqi Lv, Ning Xu, and Xin Geng. "Ambiguity-Induced Contrastive Learning for Instance-Dependent Partial Label Learning." In Thirty-First International Joint Conference on Artificial Intelligence {IJCAI-22}. California: International Joint Conferences on Artificial Intelligence Organization, 2022. http://dx.doi.org/10.24963/ijcai.2022/502.
Goyal, Palash, Divya Choudhary, and Shalini Ghosh. "Hierarchical Class-Based Curriculum Loss." In Thirtieth International Joint Conference on Artificial Intelligence {IJCAI-21}. California: International Joint Conferences on Artificial Intelligence Organization, 2021. http://dx.doi.org/10.24963/ijcai.2021/337.
Yin, Li'ang, Jianhua Han, Weinan Zhang, and Yong Yu. "Aggregating Crowd Wisdoms with Label-aware Autoencoders." In Twenty-Sixth International Joint Conference on Artificial Intelligence. California: International Joint Conferences on Artificial Intelligence Organization, 2017. http://dx.doi.org/10.24963/ijcai.2017/184.
Звіти організацій з теми "Weight labels":
Cao, Shoufeng, Uwe Dulleck, Warwick Powell, Charles Turner-Morris, Valeri Natanelov, and Marcus Foth. BeefLedger blockchain-credentialed beef exports to China: Early consumer insights. Queensland University of Technology, May 2020. http://dx.doi.org/10.5204/rep.eprints.200267.
Alonso, Pablo, Basil Kavalsky, Jose Ignacio Sembler, Hector Conroy, Salvatore Schiavo-Campo, Juan Manuel Puerta, Monika Huppi, et al. How is the IDB Serving Higher-Middle-Income Countries?: Borrowers' Perspective. Inter-American Development Bank, February 2013. http://dx.doi.org/10.18235/0010547.