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1
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.
Анотація:
Multilabel data share important features, including label imbalance, which has a significant influence on the performance of classifiers. Because of this problem, a widely used multilabel classification algorithm, the multilabel k-nearest neighbor (ML-kNN) algorithm, has poor performance on imbalanced multilabel data. To address this problem, this study proposes an improved ML-kNN algorithm based on value and weight. In this improved algorithm, labels are divided into minority and majority, and different strategies are adopted for different labels. By considering the label of latent information carried by the nearest neighbors, a value calculation method is proposed and used to directly classify majority labels. Additionally, to address the misclassification problem caused by a lack of nearest neighbor information for minority labels, weight calculation is proposed. The proposed weight calculation converts distance information with and without label sets in the nearest neighbors into weights. The experimental results on multilabel datasets from different benchmarks demonstrate the performance of the algorithm, especially for datasets with high imbalance. Different evaluation metrics show that the results are improved by approximately 2–10%. The verified algorithm could be applied to a multilabel classification of various fields involving label imbalance, such as drug molecule identification, building identification, and text categorization.
2
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.
Анотація:
Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
3
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.
Анотація:
Map labeling is a classical problem of cartography that has frequently been approached by combinatorial optimization. Given a set of features in the map and for each feature a set of label candidates, a common problem is to select an independent set of labels (that is, a labeling without label–label overlaps) that contains as many labels as possible and at most one label for each feature. To obtain solutions of high cartographic quality, the labels can be weighted and one can maximize the total weight (rather than the number) of the selected labels. We argue, however, that when maximizing the weight of the labeling, interdependences between labels are insufficiently addressed. Furthermore, in a maximum-weight labeling, the labels tend to be densely packed and thus the map background can be occluded too much. We propose extensions of an existing model to overcome these limitations. Since even without our extensions the problem is NP-hard, we cannot hope for an efficient exact algorithm for the problem. Therefore, we present a formalization of our model as an integer linear program (ILP). This allows us to compute optimal solutions in reasonable time, which we demonstrate for randomly generated instances.
4
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.
Анотація:
Each instance in multi-label data is associated with multiple labels, and there are irrelevant or redundant features in its feature space, which leads to the performance degradation of multi-label learning algorithms. Multi-label feature selection selects representative features from the feature space to improve the accuracy of the model. Due to the high cost of labels and the difficulty of data collection, there will be some missing labels in the data set, which affects the accuracy of feature selection. To solve this problem, a multi-label feature selection algorithm based on label-specific features and manifold learning is proposed. The algorithm uses the linear relationship between the features and labels in known label samples to build a linear regression model for learning label-specific features. By using the nonlinear relation between instances and the nonlinear relation between features, we can precisely learn the label-specific features. We use the Laplacian feature mapping method to construct the instance manifold model and the feature manifold model, which are also used as the regular term constraint weight matrix. The final model can not only complete the missing labels, but also select sparse and representative features. The feature selection is carried out by analyzing the weight of the feature given by the final model. Experiments were conducted to verify the effectiveness of the proposed algorithm under different label deletion rates on four evaluation indexes.
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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.
Анотація:
The real world is full of noisy labels that lead neural networks to perform poorly because deep neural networks (DNNs) are prone to overfitting label noise. Noise label training is a challenging problem relating to weakly supervised learning. The most advanced existing methods mainly adopt a small loss sample selection strategy, such as selecting the small loss part of the sample for network model training. However, the previous literature stopped here, neglecting the performance of the small loss sample selection strategy while training the DNNs, as well as the performance of different stages, and the performance of the collaborative learning of the two networks from disagreement to an agreement, and making a second classification based on this. We train the network using a comparative learning method. Specifically, a small loss sample selection strategy with dynamic weight is designed. This strategy increases the proportion of agreement based on network predictions, gradually reduces the weight of the complex sample, and increases the weight of the pure sample at the same time. A large number of experiments verify the superiority of our method.
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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.
Анотація:
For a graph G=(V,E), an edge irregular total k-labeling is a labeling of the vertices and edges of G with labels from the set {1, 2, ..., k } such that any two different edges have distinct weights. The sum of the label of edge uv and the labels of vertices u and v determines the weight of the edge uv. The smallest possible k for which the graph G has an edge irregular total k-labeling is called the total edge irregularity strength of G. We determine the exact value of the total edge irregularity strength for some cycle related graphs.
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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.
Анотація:
Purpose: To explore the psychological impact of weight labels. Design: A double-blind experiment that randomly informed participants that they were “normal weight” or “overweight.” Setting: Public university in Honolulu, Hawai‘i. Participants: Normal-weight and overweight female undergraduates (N = 113). Measures: The Body Image States Scale, Stunkard Rating Scale, Weight Bias Internalization Scale, Positive and Negative Affect Schedule, General Health question from the 12-item Short Form Health Survey, modified version of the Weight Loss Methods Scale, and a manipulation check. Analysis: A 2 × 2 between-subjects analysis of variance explored the main effects of the assigned weight label and actual weight and interactions between assigned weight label and actual weight. Results: Significant main effects of the assigned weight label emerged on measures of body dissatisfaction, F(1, 109) = 12.40, p = .001, [Formula: see text] = 0.10, internalized weight stigma, F(1, 108) = 4.35, p = .039, [Formula: see text] = .04, and negative affect, F(1, 108) = 9.22, p = .003, [Formula: see text] = .08. Significant assigned weight label × actual weight interactions were found on measures of perceived body image, F(1, 109) = 6.29, p = .014, [Formula: see text] = .06, and perceived health, F(1, 109) = 4.18, p = .043, [Formula: see text] = .04. Conclusion: A weight label of “overweight” may have negative psychological consequences, particularly for overweight women.
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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.
Анотація:
AbstractIntroduction:The aim of this study was to investigate the usability of the age value listed on the labels on children’s clothes in the age-based weight estimation method recommended by the Pediatric Advanced Life Support (PALS) guidelines.Material-Method:This prospective, cross-sectional study was organized in Antalya Training and Research Hospital Emergency Department. Children aged between 1-12 years were included in the study. The weight measurements of the children were obtained based on the age-related criteria on the labels of their clothes. The estimated values were compared with the real values of the cases measured on the scale.Results:One-thousand ninety-four cases were included, the mean age of cases in age-based measurements was 6.25 years, which was 6.5 years in label-based measurements. Average weights measured 25.75kg according to age-based measurements, 26.5kg according to label-based measurements, and 26.0kg on the scales, and showed no statistical difference (P <.0001). It was estimated that 741 (67.7%) of age-based measurements and 775 (70.8%) of label-based measurements were within (±)10% values within the normal measurement limits and no significant difference was measured.Conclusion:In the emergency department and prehospital setting, children with an unknown age and that need resuscitation and interventional procedures for stabilization, and have no time for weight estimation, checking the age on clothing label (ACL) instead of the actual age (AA) can be safely used for the age-dependent weight calculation formula recommended by the PALS guide.
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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.
Анотація:
Multi-label streaming feature selection has received widespread attention in recent years because the dynamic acquisition of features is more in line with the needs of practical application scenarios. Most previous methods either assume that the labels are independent of each other, or, although label correlation is explored, the relationship between related labels and features is difficult to understand or specify. In real applications, both situations may occur where the labels are correlated and the features may belong specifically to some labels. Moreover, these methods treat features individually without considering the interaction between features. Based on this, we present a novel online streaming feature selection method based on label group correlation and feature interaction (OSLGC). In our design, we first divide labels into multiple groups with the help of graph theory. Then, we integrate label weight and mutual information to accurately quantify the relationships between features under different label groups. Subsequently, a novel feature selection framework using sliding windows is designed, including online feature relevance analysis and online feature interaction analysis. Experiments on ten datasets show that the proposed method outperforms some mature MFS algorithms in terms of predictive performance, statistical analysis, stability analysis, and ablation experiments.
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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.
Анотація:
Following the method of Bourdieu’s Distinction (1984) and especially The Weight of the World (1999), this article presents interviews with four Spanish record labels, which provide case studies of the workings of the field. Distinction shows that uses of culture are affected by social class. The Weight of the World presents lightly edited interviews with marginalized groups in France. The interviews presented in this article attempt to relate the lifestyle or class habitus of the person interviewed to their strategies of operating a punk record label. The recorded interviews also provide a great deal of concrete information on independent punk labels in Spain.
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Haidar, Amier, Felicia R. Carey, Nalini Ranjit, Natalie Archer, and Deanna Hoelscher. "Self-reported use of nutrition labels to make food choices is associated with healthier dietary behaviours in adolescents." Public Health Nutrition 20, no. 13 (July 14, 2017): 2329–39. http://dx.doi.org/10.1017/s1368980017001252.
Анотація:
AbstractObjectiveThe study aimed to examine nutrition label use and dietary behaviours among ethnically diverse middle- and high-school students, in Texas, USA.DesignThe School Physical Activity and Nutrition (SPAN) survey is a cross-sectional statewide study using a self-administered questionnaire to assess nutrition and physical activity behaviours. Height and weight measurements were used to determine BMI. Multivariable logistic regression was used to determine associations between nutrition label use and dietary behaviours, with gender, grade, ethnicity, BMI, parent education, socio-economic status and nutrition knowledge as covariates.SettingParticipants from 283 schools, weighted to represent Texas youth.SubjectsSPAN 2009–2011 included 6716 8th and 11th graders (3465 girls and 3251 boys). The study population consisted of 39·83 % White/Other, 14·61 % African-American and 45·56 % Hispanic adolescents; with a mean age of 14·9 years, and 61·95 % at a healthy weight, 15·71 % having overweight and 22·34 % having obesity.ResultsAdolescents who did not use nutrition labels had 1·69 times greater odds of consuming ≥1 sugary beverages/d (P<0·05). Adolescents who used nutrition labels had 2·13 times greater odds of consuming ≥1 fruits and vegetables/d (P<0·05). Adolescents who used nutrition labels had significantly higher healthy eating scores than those who did not (P<0·001). For every 1-point increase in nutrition knowledge, adolescents had 1·22 greater odds of using nutrition labels.ConclusionsNutrition label use is associated with healthier dietary behaviours in adolescents. Intervention strategies for youth should include efforts to teach adolescents to use labels to make healthy food choices.
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Bača, Martin, Muhammad Imran, Zuzana Kimáková, and Andrea Semaničová-Feňovčíková. "A new generalization of edge-irregular evaluations." AIMS Mathematics 8, no. 10 (2023): 25249–61. http://dx.doi.org/10.3934/math.20231287.
Анотація:
<abstract><p>Consider a simple graph $ G = (V, E) $ of size $ m $ with the vertex set $ V $ and the edge set $ E $. A modular edge-irregular total $ k $-labeling of a graph $ G $ is a labeling scheme for the vertices and edges with the labels $ 1, 2, \dots, k $ that allows the modular weights of any two different edges to be distinct, where the modular weight of an edge is the remainder of the division of the weight (i.e., the sum of the label of the edge itself and the labels of its two end vertices) by $ m $. The maximal integer $ k $, minimized over all modular edge-irregular total $ k $-labelings of the graph $ G $ is called the modular total edge-irregularity strength. In the paper, we generalize the approach to edge-irregular evaluations, introduce the notion of the modular total edge-irregularity strength and obtain its boundary estimation. For certain families of graphs, we investigate the existence of modular edge-irregular total labelings and determine the precise values of the modular total edge-irregularity strength in order to prove the sharpness of the lower bound.</p></abstract>
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Shah, Meena, Brooke Bouza, Beverley Adams-Huet, Manall Jaffery, Phil Esposito, and Lyn Dart. "Effect of calorie or exercise labels on menus on calories and macronutrients ordered and calories from specific foods in Hispanic participants: a randomized study." Journal of Investigative Medicine 64, no. 8 (July 8, 2016): 1261–68. http://dx.doi.org/10.1136/jim-2016-000227.
Анотація:
The effect of menu labels on food choices is unknown in Hispanics. This study evaluated the impact of menu labels on calories and macronutrients ordered in Hispanics. 372 Hispanics (18–65 years) were randomly assigned to menus with no labels (NL) (n=127), rank-ordered calorie labels plus a statement on energy needs per meal (CL) (n=123), or rank-ordered exercise labels showing minutes of brisk walking necessary to burn the food calories (EL) (n=122). The menus had identical food choices. Participants were instructed to select foods from the assigned menu as if having lunch in a fast food restaurant. One-way analysis of variance found no difference in calories ordered (median (25th and 75th centiles)) by menu condition (NL: 785.0 (465.0, 1010.0) kcal; CL: 790.0 (510.0, 1020.0) kcal; EL: 752.5 (520.0, 1033.8) kcal; p=0.75). Calories from specific foods and macronutrient intake were not different by menu condition. Menu label use was 26.8% in the CL and 25.4% in the EL condition. Calories ordered were not different between those who used and those who did not use the labels. Regression analysis showed that perception of being overweight (p=0.02), selecting foods based on health value (p<0.0001), and meeting exercise guidelines (p<0.0001) were associated with fewer calories ordered. Logistic regression showed that selecting foods based on health value (p=0.01) was associated with higher food label use. Menu labels did not affect food choices in Hispanic participants. Future studies should determine if nutrition, exercise, and weight perception counseling prior to menu labels intervention would result in better food choices.Trial registration numberNCT02804503; post-results.
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Zheng, Kecheng, Cuiling Lan, Wenjun Zeng, Zhizheng Zhang, and Zheng-Jun Zha. "Exploiting Sample Uncertainty for Domain Adaptive Person Re-Identification." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 4 (May 18, 2021): 3538–46. http://dx.doi.org/10.1609/aaai.v35i4.16468.
Анотація:
Many unsupervised domain adaptive (UDA) person ReID approaches combine clustering-based pseudo-label prediction with feature fine-tuning. However, because of domain gap, the pseudo-labels are not always reliable and there are noisy/incorrect labels. This would mislead the feature representation learning and deteriorate the performance. In this paper, we propose to estimate and exploit the credibility of the assigned pseudo-label of each sample to alleviate the influence of noisy labels, by suppressing the contribution of noisy samples. We build our baseline framework using the mean teacher method together with an additional contrastive loss. We have observed that a sample with a wrong pseudo-label through clustering in general has a weaker consistency between the output of the mean teacher model and the student model. Based on this finding, we propose to exploit the uncertainty (measured by consistency levels) to evaluate the reliability of the pseudo-label of a sample and incorporate the uncertainty to re-weight its contribution within various ReID losses, including the ID classification loss per sample, the triplet loss, and the contrastive loss. Our uncertainty-guided optimization brings significant improvement and achieves the state-of-the-art performance on benchmark datasets.
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Crawford, Cindy, Bharathi Avula, Andrea T. Lindsey, Kumar Katragunta, Ikhlas A. Khan, and Patricia A. Deuster. "Label Accuracy of Weight Loss Dietary Supplements Marketed Online With Military Discounts." JAMA Network Open 7, no. 5 (May 1, 2024): e249131. http://dx.doi.org/10.1001/jamanetworkopen.2024.9131.
Анотація:
ImportanceDietary supplements for weight loss, among the most popular supplement products on the market, are promoted not only for losing weight and shedding fat, but also for added benefits of energy and performance, all packed into 1 capsule with multiple combinations of ingredients. Fraudulent marketing of weight loss supplements, some with exaggerated claims, some that are potentially dangerous, and some that contain illegal ingredients, is ever present, especially through online sources, where multiple manufacturers target service members by offering military discounts.ObjectivesTo examine whether select dietary supplements marketed online for weight loss from companies advertising military discounts are accurately labeled according to the Supplement Facts listed ingredients, whether they contain any ingredients prohibited for use in the military, and to qualitatively describe the products’ label claims.Design, Setting, and ParticipantsIn this case series, 30 dietary supplement products marketed for weight loss were selected and purchased in June 2023 from 12 online companies advertising military discounts. Data were analyzed from July to August 2023.Main Outcomes and MeasuresLiquid chromatography-mass spectrometry was used to verify whether products were accurately labeled according to the Supplement Facts listed ingredients and whether they contained any substances on the DoD Prohibited Dietary Supplement Ingredients List. A separate analysis was conducted to describe product label claims by using the Operation Supplement Safety (OPSS) Risk Assessment Scorecard.ResultsOf the 30 products tested, analysis showed that 25 had inaccurate labels. Of these, 24 had ingredients listed on the label that were not detected (misbranded); 7 had hidden components not present on the label, some of which would be considered adulterated; and 10 had substances on the DoD Prohibited Dietary Supplement Ingredients List either on or hidden from the label. All products were rated as risky when applying the OPSS Scorecard.Conclusions and RelevanceIn this case series study, the majority of products had inaccurate labels. Some were misbranded, others would be considered adulterated with ingredients not allowed in dietary supplements, and some contained ingredients prohibited for use in the military.
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Gao, Yuefang, Yiteng Cai, Xuanming Bi, Bizheng Li, Shunpeng Li, and Weiping Zheng. "Cross-Domain Facial Expression Recognition through Reliable Global–Local Representation Learning and Dynamic Label Weighting." Electronics 12, no. 21 (November 6, 2023): 4553. http://dx.doi.org/10.3390/electronics12214553.
Анотація:
Cross-Domain Facial Expression Recognition (CD-FER) aims to develop a facial expression recognition model that can be trained in one domain and deliver consistent performance in another. CD-FER poses a significant challenges due to changes in marginal and class distributions between source and target domains. Existing methods primarily emphasize achieving domain-invariant features through global feature adaptation, often neglecting the potential benefits of transferable local features across different domains. To address this issue, we propose a novel framework for CD-FER that combines reliable global–local representation learning and dynamic label weighting. Our framework incorporates two key modules: the Pseudo-Complementary Label Generation (PCLG) module, which leverages pseudo-labels and complementary labels obtained using a credibility threshold to learn domain-invariant global and local features, and the Label Dynamic Weight Matching (LDWM) module, which assesses the learning difficulty of each category and adaptively assigns corresponding label weights, thereby enhancing the classification performance in the target domain. We evaluate our approach through extensive experiments and analyses on multiple public datasets, including RAF-DB, FER2013, CK+, JAFFE, SFW2.0, and ExpW. The experimental results demonstrate that our proposed model outperforms state-of-the-art methods, with an average accuracy improvement of 3.5% across the five datasets.
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Ekadipta, Isngunaenah, and Fauziah Purnama Sari. "ANALISA KESESUAIAN STANDAR LABEL PANGAN PADA KEMASAN PRODUK BISKUIT LOKAL DAN IMPOR TEREGISTRASI DI BADAN PENGAWAS OBAT DAN MAKANAN." ISTA Online Technologi Journal 2, no. 1 (February 22, 2021): 1–7. http://dx.doi.org/10.62702/ion.v2i1.31.
Анотація:
Abstrak Hal yang mendasari penelitian ini karena banyak ditemukan label kemasan yang beredar tidak sesuai standar Peraturan Pemerintah No. 69 Tahun 1999. Berdasarkan Peraturan Pemerintah No 69 Tahun 1999 tentang label pangan mencakup lima unsur yang harus dipenuhi dalam pencantuman informasi pada label kemasan. Tujuan penelitian ini dilakukan untuk mengetahui kesesuaian label kemasan produk biskuit lokal dan impor serta mengevaluasi pelanggaran yang terjadi pada label kemasan produk biskuit lokal dan impor yang mencakup lima unsur teknis. Metode penelitian yang digunakan metode analisis deskriptif non analitik dengan jumlah sampel 139 kemasan produk biskuit lokal dan impor yang dibandingkan dengan PP No 69 Tahun 1999. Berdasarkan hasil pengamatan diperoleh persentase yang mencakup lima unsur yaitu unsur teknis pencantuman label (85.7%), unsur tulisan pada label (87.0%), unsur keterangan label pada bagian utama (82.2%),unsur keterangan label pada bagian lain (68.5%), dan unsur keterangan yang dilarang (99.0%). Pelanggaran yang ditemukan antara lain label mudah luntur atau rusak, label tidak diletakkan pada tempat yang mudah terbaca, tulisan pada label yang tidak jelas, nama produk pangan yang tidak dicantumkan dan tidak sesuai dengan yang teregistrasi, pencantuman komposisi yang berbayang, tidak menggunakan bahasa Indonesia, pencantuman tanggal kadaluarsa yang mudah luntur, pencantuman nomor pendaftaran produk pangan yang tidak terdaftar dalam website resmi BPOM (Badan Pengawas Obat dan Makanan), pencantuman berat bersih, nama dan alamat produsen/importir, kode produksi, dan informasi kandungan gizi yang belum sesuai standar. Abstract The basis this study, because there are still many packaging label on the market not accordance with the standards the Government Regulation No. 69 Years 1999. Based on the Goverment Regulation No. 69 Years 1999 about the labels includes five elements information must declare on the packaging label. Has conducted research related to analysis the suitable the standard packaging label local and import biscuits product registered at the National Agency of Drug and Food Control. The research is aimed to analyze packaging labels in biscuit local and import and evaluate violation that happened in the packaging labels in biscuit local and import include five elements information. The study used a descriptive analysis non-analytic method with sample of 139 biscuits local and import as compared with Regulation No. 69 Years 1999. According the observations obtained the percentage included five elements, technical elements on the labels (85.7%), technical writen on the labels (87.0%), technical statement on the main labels (82.2%), technical statement on the other labels (68.5%), and techical statement not declare (99.0%). Violations were found consist of labels easily worn or damage, the labels are not put in place that is easy to read, the writing on the label is not clear, the name food product that is not listed and not registered, the ingredients shaded, not use Indonesian language, declare expiry date easily fade, declare approval number not list in BPOM website, declare net weight, name and address producer/importer, production code, and information nutrition fact not accordance with the standard.
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MANDAL, BIDISHA. "Use of Food Labels as a Weight Loss Behavior." Journal of Consumer Affairs 44, no. 3 (September 2010): 516–27. http://dx.doi.org/10.1111/j.1745-6606.2010.01181.x.
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Suo, M., S. Li, Y. Chen, Z. Zhang, B. Zhu, and R. An. "Effectiveness evaluation of fighter using fuzzy Bayes risk weighting method." Aeronautical Journal 122, no. 1254 (June 4, 2018): 1275–300. http://dx.doi.org/10.1017/aer.2018.54.
Анотація:
ABSTRACTMultiple Attribute Decision Analysis (MADA), known to be simple and convenient, is one of the most commonly used methods for Effectiveness Evaluation of Fighter (EEF), in which the attribute weight assignment plays a key role. Generally, there are two parts in the index system of MADA, i.e. performance index and decision index (or label), which denote the specific performance and the category of the object, respectively. In some index systems of EEF, the labels can be easily obtained, which are presented as the generations of fighters. However, the existing methods of attribute weight determination usually ignore or do not take full advantage of the supervisory function of labels. To make up for this deficiency, this paper develops an objective method based on fuzzy Bayes risk. In this method, a loss function model based on Gaussian kernel function is proposed to cope with the drawback that the loss function in Bayes risk is usually determined by experts. In order to evaluate the credibility of assigned weights, a longitudinal deviation and transverse residual correlation coefficient model is designed. Finally, a number of experiments, including the comparison experiments on University of California Irvine (UCI) data and EEF, are carried out to illustrate the superiority and applicability of the proposed method.
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Bača, Martin, Andrea Semaničová-Feňovčíková, and Tao-Ming Wang. "Local Antimagic Chromatic Number for Copies of Graphs." Mathematics 9, no. 11 (May 27, 2021): 1230. http://dx.doi.org/10.3390/math9111230.
Анотація:
An edge labeling of a graph G=(V,E) using every label from the set {1,2,⋯,|E(G)|} exactly once is a local antimagic labeling if the vertex-weights are distinct for every pair of neighboring vertices, where a vertex-weight is the sum of labels of all edges incident with that vertex. Any local antimagic labeling induces a proper vertex coloring of G where the color of a vertex is its vertex-weight. This naturally leads to the concept of a local antimagic chromatic number. The local antimagic chromatic number is defined to be the minimum number of colors taken over all colorings of G induced by local antimagic labelings of G. In this paper, we estimate the bounds of the local antimagic chromatic number for disjoint union of multiple copies of a graph.
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Ding, Qianggang, Sifan Wu, Tao Dai, Hao Sun, Jiadong Guo, Zhang-Hua Fu, and Shutao Xia. "Knowledge Refinery: Learning from Decoupled Label." Proceedings of the AAAI Conference on Artificial Intelligence 35, no. 8 (May 18, 2021): 7228–35. http://dx.doi.org/10.1609/aaai.v35i8.16888.
Анотація:
Recently, a variety of regularization techniques have been widely applied in deep neural networks, which mainly focus on the regularization of weight parameters to encourage generalization effectively. Label regularization techniques are also proposed with the motivation of softening the labels while neglecting the relation of classes. Among them, the technique of knowledge distillation proposes to distill the soft label, which contains the knowledge of class relations. However, this technique needs to pre-train an extra cumbersome teacher model. In this paper, we propose a method called Knowledge Refinery (KR), which enables the neural network to learn the relation of classes on-the-fly without the teacher-student training strategy. We propose the definition of decoupled labels, which consist of the original hard label and the residual label. To exhibit the generalization of KR, we evaluate our method in both fields of computer vision and natural language processing. Our empirical results show consistent performance gains under all experimental settings.
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Graham, Dan J., and Robert W. Jeffery. "Predictors of nutrition label viewing during food purchase decision making: an eye tracking investigation." Public Health Nutrition 15, no. 2 (July 7, 2011): 189–97. http://dx.doi.org/10.1017/s1368980011001303.
Анотація:
AbstractObjectiveNutrition label use could help consumers eat healthfully. Despite consumers reporting label use, diets are not very healthful and obesity rates continue to rise. The present study investigated whether self-reported label use matches objectively measured label viewing by monitoring the gaze of individuals viewing labels.DesignThe present study monitored adults viewing sixty-four food items on a computer equipped with an eye-tracking camera as they made simulated food purchasing decisions. ANOVA and t tests were used to compare label viewing across various subgroups (e.g. normal weight v. overweight v. obese; married v. unmarried) and also across various types of foods (e.g. snacks v. fruits and vegetables).SettingParticipants came to the University of Minnesota's Epidemiology Clinical Research Center in spring 2010.SubjectsThe 203 participants were ⩾18 years old and capable of reading English words on a computer 76 cm (30 in) away.ResultsParticipants looked longer at labels for ‘meal’ items like pizza, soup and yoghurt compared with fruits and vegetables, snack items like crackers and nuts, and dessert items like ice cream and cookies. Participants spent longer looking at labels for foods they decided to purchase compared with foods they decided not to purchase. There were few between-group differences in nutrition label viewing across sex, race, age, BMI, marital status, income or educational attainment.ConclusionsNutrition label viewing is related to food purchasing, and labels are viewed more when a food's healthfulness is ambiguous. Objectively measuring nutrition label viewing provides new insight into label use by various sociodemographic groups.
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Lu, Yang, Lin Chen, Yonggang Zhang, Yiliang Zhang, Bo Han, Yiu-ming Cheung, and Hanzi Wang. "Federated Learning with Extremely Noisy Clients via Negative Distillation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 13 (March 24, 2024): 14184–92. http://dx.doi.org/10.1609/aaai.v38i13.29329.
Анотація:
Federated learning (FL) has shown remarkable success in cooperatively training deep models, while typically struggling with noisy labels. Advanced works propose to tackle label noise by a re-weighting strategy with a strong assumption, i.e., mild label noise. However, it may be violated in many real-world FL scenarios because of highly contaminated clients, resulting in extreme noise ratios, e.g., >90%. To tackle extremely noisy clients, we study the robustness of the re-weighting strategy, showing a pessimistic conclusion: minimizing the weight of clients trained over noisy data outperforms re-weighting strategies. To leverage models trained on noisy clients, we propose a novel approach, called negative distillation (FedNed). FedNed first identifies noisy clients and employs rather than discards the noisy clients in a knowledge distillation manner. In particular, clients identified as noisy ones are required to train models using noisy labels and pseudo-labels obtained by global models. The model trained on noisy labels serves as a ‘bad teacher’ in knowledge distillation, aiming to decrease the risk of providing incorrect information. Meanwhile, the model trained on pseudo-labels is involved in model aggregation if not identified as a noisy client. Consequently, through pseudo-labeling, FedNed gradually increases the trustworthiness of models trained on noisy clients, while leveraging all clients for model aggregation through negative distillation. To verify the efficacy of FedNed, we conduct extensive experiments under various settings, demonstrating that FedNed can consistently outperform baselines and achieve state-of-the-art performance.
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Pan, Junwen, Qi Bi, Yanzhan Yang, Pengfei Zhu, and Cheng Bian. "Label-Efficient Hybrid-Supervised Learning for Medical Image Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 2 (June 28, 2022): 2026–34. http://dx.doi.org/10.1609/aaai.v36i2.20098.
Анотація:
Due to the lack of expertise for medical image annotation, the investigation of label-efficient methodology for medical image segmentation becomes a heated topic. Recent progresses focus on the efficient utilization of weak annotations together with few strongly-annotated labels so as to achieve comparable segmentation performance in many unprofessional scenarios. However, these approaches only concentrate on the supervision inconsistency between strongly- and weakly-annotated instances but ignore the instance inconsistency inside the weakly-annotated instances, which inevitably leads to performance degradation. To address this problem, we propose a novel label-efficient hybrid-supervised framework, which considers each weakly-annotated instance individually and learns its weight guided by the gradient direction of the strongly-annotated instances, so that the high-quality prior in the strongly-annotated instances is better exploited and the weakly-annotated instances are depicted more precisely. Specially, our designed dynamic instance indicator (DII) realizes the above objectives, and is adapted to our dynamic co-regularization (DCR) framework further to alleviate the erroneous accumulation from distortions of weak annotations. Extensive experiments on two hybrid-supervised medical segmentation datasets demonstrate that with only 10% strong labels, the proposed framework can leverage the weak labels efficiently and achieve competitive performance against the 100% strong-label supervised scenario.
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Huang, Wenzhi, Junchi Zhang, and Donghong Ji. "Extracting Chinese events with a joint label space model." PLOS ONE 17, no. 9 (September 27, 2022): e0272353. http://dx.doi.org/10.1371/journal.pone.0272353.
Анотація:
The task of event extraction consists of three subtasks namely entity recognition, trigger identification and argument role classification. Recent work tackles these subtasks jointly with the method of multi-task learning for better extraction performance. Despite being effective, existing attempts typically treat labels of event subtasks as uninformative and independent one-hot vectors, ignoring the potential loss of useful label information, thereby making it difficult for these models to incorporate interactive features on the label level. In this paper, we propose a joint label space framework to improve Chinese event extraction. Specifically, the model converts labels of all subtasks into a dense matrix, giving each Chinese character a shared label distribution via an incrementally refined attention mechanism. Then the learned label embeddings are also used as the weight of the output layer for each subtask, hence adjusted along with model training. In addition, we incorporate the word lexicon into the character representation in a soft probabilistic manner, hence alleviating the impact of word segmentation errors. Extensive experiments on Chinese and English benchmarks demonstrate that our model outperforms state-of-the-art methods.
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Jacobs, Tom G., Hylke Waalewijn, and Angela Colbers. "Use of WHO paediatric weight-band dosing in drug labels." Lancet HIV 9, no. 1 (January 2022): e3-e4. http://dx.doi.org/10.1016/s2352-3018(21)00277-0.
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De Silva, Devarahandhi Achini Melda, Renda Kankanamge Chaturika Jeewanthi, Rajapakshage Heshani Navoda Rajapaksha, Weddagala Mudiyanselage Tharaka Bilindu Weddagala, Naoki Hirotsu, Bun-ichi Shimizu, and Munasinghe Arachchige Jagath Priyantha Munasinghe. "Clean vs dirty labels: Transparency and authenticity of the labels of Ceylon cinnamon." PLOS ONE 16, no. 11 (November 23, 2021): e0260474. http://dx.doi.org/10.1371/journal.pone.0260474.
Анотація:
Ceylon cinnamon, which was regarded as a luxury spice during ancient times, has been consumed for its medicinal properties and health benefits for thousands of years. For centuries, Arabian traders controlled the European cinnamon trade through limited supplies from a country which they did not reveal. Content marketing analysis and chemical profiling of value-added products of Ceylon cinnamon in the global marketplace are proposed to investigate the clean status of the product labels. In the present study, a mixed-method approach was employed to investigate the labels of 6 types of value-added forms of cinnamon; i.e. quills, powder, tea, breakfast cereals, confectionery and bakery and nutraceuticals which are used in USA, UK, Mexico, Japan and products of Sri Lankan cinnamon exporters. Two hundred and seventy-six labels were analyzed to find out the aspects of clean status, transparency and authenticity. Key label claims of the cinnamon products lie within the bounds of cleaner, healthy, nutritional and sustainable attributes. Consumer perception lies within ingredients, nutritional value, country of origin and claim on safety and quality standards and certification. The value chain transparency, ethical rules (species mislabeling), and chemical profile of the pharmaceutical, confectionery and fragrance industry inputs were ignored. The best claim and competitive advantage of the Ceylon cinnamon; an ultra-low level (<0.01 mg/g Dry Weight) of Coumarin, were rarely indicated in labels. Lack of clean labels and traceability lagged Ceylon cinnamon in the 40 international markets while Cassia cinnamon (Coumarin content 2.23 mg/g DW), a major competitor of Ceylon cinnamon appears in the market with dirty labels. Millennials and upper-middle-class female consumers in their active ages, place a high demand on Ceylon cinnamon. Today’s tech-savvy global consumers of Ceylon cinnamon use market intelligence frequently for identifying product authenticity. Well equipped clean labels were found to be demanded by the modern cinnamon consumers.
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Sun, Heli, Jianbin Huang, Xiang Zhong, Ke Liu, Jianhua Zou та Qinbao Song. "Label Propagation withα-Degree Neighborhood Impact for Network Community Detection". Computational Intelligence and Neuroscience 2014 (2014): 1–9. http://dx.doi.org/10.1155/2014/130689.
Анотація:
Community detection is an important task for mining the structure and function of complex networks. In this paper, a novel label propagation approach withα-degree neighborhood impact is proposed for efficiently and effectively detecting communities in networks. Firstly, we calculate the neighborhood impact of each node in a network within the scope of itsα-degree neighborhood network by using an iterative approach. To mitigate the problems of visiting order correlation and convergence difficulty when updating the node labels asynchronously, our method updates the labels in an ascending order on theα-degree neighborhood impact of all the nodes. Theα-degree neighborhood impact is also taken as the updating weight value, where the parameter impact scopeαcan be set to a positive integer. Experimental results from several real-world and synthetic networks show that our method can reveal the community structure in networks rapidly and accurately. The performance of our method is better than other label propagation based methods.
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Malekzadeh, Milad, and Jed A. Long. "A network community structure similarity index for weighted networks." PLOS ONE 18, no. 11 (November 29, 2023): e0292018. http://dx.doi.org/10.1371/journal.pone.0292018.
Анотація:
Identification of communities in complex systems is an essential part of network analysis. Accordingly, measuring similarities between communities is a fundamental part of analysing community structure in different, yet related, networks. Commonly used methods for quantifying network community similarity fail to consider the effects of edge weights. Existing methods remain limited when the two networks being compared have different numbers of nodes. In this study, we address these issues by proposing a novel network community structure similarity index (NCSSI) based on the edit distance concept. NCSSI is proposed as a similarity index for comparing network communities. The NCSSI incorporates both community labels and edge weights. The NCSSI can also be employed to assess the similarity between two communities with varying numbers of nodes. We test the proposed method using simulated data and case-study analysis of New York Yellow Taxi flows and compare the results with that of other commonly used methods (i.e., mutual information and the Jaccard index). Our results highlight how NCSSI effectively captures the impact of both label and edge weight changes and their impacts on community structure, which are not captured in existing approaches. In conclusion, NCSSI offers a new approach that incorporates both label and weight variations for community similarity measurement in complex networks.
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Lee, Yujin, David Kim, Junxiu Liu, Matti Marklund, Parke Wilde, Dariush Mozaffarian, John Wong, and Renata Micha. "Health and Economic Impacts of a Sugar-Sweetened Beverage Warning Label in the US: A Micro-Simulation Study." Current Developments in Nutrition 4, Supplement_2 (May 29, 2020): 715. http://dx.doi.org/10.1093/cdn/nzaa051_012.
Анотація:
Abstract Objectives Sugar-sweetened beverage (SSB) consumption is linked to weight gain, type 2 diabetes and cardiovascular disease (CVD). Health warning labels on SSBs have been proposed in the US to discourage consumption. Yet, the potential health and economic impacts of SSB warning labels have not been quantified. We aimed to estimate the health and economic impacts of a SSB warning label in the US. Methods We used the validated Tufts Diabetes-CVD Microsimulation Model to estimate the impact of implementing a national SSB warning label in the U.S., compared to current status quo, on incident diabetes and CVD. Model inputs included nationally representative demographic, clinical, and SSB intake data from NHANES 2015–2016; policy effects on consumer intakes and SSB-disease effects from meta-analyses; disease data from CDC wonder database; and policy implementation costs and healthcare costs from established sources. Findings were evaluated over 10 years and a lifetime horizon, and costs (in 2019 USD) discounted at 3% annually. NHANES sampling weights were used to translate model estimates to nationally representative population estimates; and alternative scenarios evaluated smaller policy effects on consumer consumption, derived from prior interventional studies testing effects of SSB warning labels. Results Among 138 million US adults aged 40–79 years at baseline, 56% were SSB consumers, with mean intake of 1.10 servings/day (95% CI, 0.97, 1.23). Over 10 years, the SSB warning label was estimated to prevent 254 thousand (145, 362) incident CVD events and 231 thousand (–45, 507) diabetes cases, with $30.6 billion (29.2, 32.0) savings in healthcare costs. Over lifetime, corresponding values were 708 thousand (328, 1087), 422 thousand (77, 767), and $78.3 billion (43.8, 112.8), respectively. In sensitivity analyses with a 40% smaller policy effect size, corresponding lifetime values were 348 thousand (38, 658), 246 thousand (–98, 590), and $40.8 billion (31.5, 50.0) over a lifetime, respectively. Conclusions Implementing a national SSB warning label could generate substantial health gains and cost savings for the US population. Funding Sources NIH, NHLBI.
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Sudibyo, Nugroho Arif, and Siti Komsatun. "PELABELAN TOTAL TAK REGULER PADA BEBERAPA GRAF." Jurnal Ilmiah Matematika dan Pendidikan Matematika 10, no. 2 (December 28, 2018): 9. http://dx.doi.org/10.20884/1.jmp.2018.10.2.2840.
Анотація:
For a simple graph G with vertex set V (G) and edge set E(G), a labeling $\Phi:V(G)\cup U(G)\rightarrow\{1,2,...k\}$ is called a vertex irregular total k- labeling of G if for any two diferent vertices x and y, their weights wt(x) and wt(y) are distinct. The weight wt(x) of a vertex x in G is the sum of its label and the labels of all edges incident with the given vertex x. The total vertex irregularity strength of G, tvs(G), is the smallest positive integer k for which G has a vertex irregular total k-labeling. In this paper, we study the total vertex irregularity strength of some class of graph.
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Tarlowski, Andrzej. "Naming Patterns and Inductive Inference: The Case of Birds." Journal of Cognition and Culture 11, no. 1-2 (2011): 189–216. http://dx.doi.org/10.1163/156853711x568743.
Анотація:
AbstractAlthough past research demonstrated that online presentation of labels plays a role in inductive inference few studies have shown that naming practices affect stable category representations that enter into inductive judgments. In this study we provide evidence for a relationship between naming and inductive inference by examining Polish and Spanish speakers’ inferences within the taxonomic class Aves. Birds in Polish are named with one label, ptak, while Spanish uses two labels, ave and pájaro. Size is the feature that determines whether Spanish speakers label a bird as ave or pájaro. As a result, compared to Polish speakers, Spanish speakers attach higher weight to bird size. This is evidenced by the fact that Spanish speakers’ perception of strength of inferences from birds decreases more strongly as a function of size dissimilarity between premise and conclusion. The hypothesis that feature weighting mediates in the influence of naming on induction is supported by the cross-linguistic differences in perceptions of animal similarity. The set of findings reported here contributes to the understanding of inductive inference and the relationship between language and thought.
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An, Pei, Junxiong Liang, Xing Hong, Siwen Quan, Tao Ma, Yanfei Chen, Liheng Wang, and Jie Ma. "Leveraging Self-Paced Semi-Supervised Learning with Prior Knowledge for 3D Object Detection on a LiDAR-Camera System." Remote Sensing 15, no. 3 (January 20, 2023): 627. http://dx.doi.org/10.3390/rs15030627.
Анотація:
Three dimensional (3D) object detection with an optical camera and light detection and ranging (LiDAR) is an essential task in the field of mobile robot and autonomous driving. The current 3D object detection method is based on deep learning and is data-hungry. Recently, semi-supervised 3D object detection (SSOD-3D) has emerged as a technique to alleviate the shortage of labeled samples. However, it is still a challenging problem for SSOD-3D to learn 3D object detection from noisy pseudo labels. In this paper, to dynamically filter the unreliable pseudo labels, we first introduce a self-paced SSOD-3D method SPSL-3D. It exploits self-paced learning to automatically adjust the reliability weight of the pseudo label based on its 3D object detection loss. To evaluate the reliability of the pseudo label in accuracy, we present prior knowledge based SPSL-3D (named as PSPSL-3D) to enhance the SPSL-3D with the semantic and structure information provided by a LiDAR-camera system. Extensive experimental results in the public KITTI dataset demonstrate the efficiency of the proposed SPSL-3D and PSPSL-3D.
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Czyżycki, Tomasz, Jiří Hrivnák, and Lenka Motlochová. "Generalized Dual-Root Lattice Transforms of Affine Weyl Groups." Symmetry 12, no. 6 (June 16, 2020): 1018. http://dx.doi.org/10.3390/sym12061018.
Анотація:
Discrete transforms of Weyl orbit functions on finite fragments of shifted dual root lattices are established. The congruence classes of the dual weight lattices intersected with the fundamental domains of the affine Weyl groups constitute the point sets of the transforms. The shifted weight lattices intersected with the fundamental domains of the extended dual affine Weyl groups form the sets of labels of Weyl orbit functions. The coinciding cardinality of the point and label sets and corresponding discrete orthogonality relations of Weyl orbit functions are demonstrated. The explicit counting formulas for the numbers of elements contained in the point and label sets are calculated. The forward and backward discrete Fourier-Weyl transforms, together with the associated interpolation and Plancherel formulas, are presented. The unitary transform matrices of the discrete transforms are exemplified for the case A 2 .
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Yan, Xuesong, Qinghua Wu, and Victor S. Sheng. "A Double Weighted Naive Bayes with Niching Cultural Algorithm for Multi-Label Classification." International Journal of Pattern Recognition and Artificial Intelligence 30, no. 06 (May 9, 2016): 1650013. http://dx.doi.org/10.1142/s0218001416500130.
Анотація:
Multi-label classification is to assign an instance to multiple classes. Naive Bayes (NB) is one of the most popular algorithms for pattern recognition and classification. It has a high performance in single label classification. It is naturally extended for multi-label classification under the assumption of label independence. As we know, NB is based on a simple but unrealistic assumption that attributes are conditionally independent given the class. Therefore, a double weighted NB (DWNB) is proposed to demonstrate the influences of predicting different labels based on different attributes. Our DWNB utilizes the niching cultural algorithm (NLA) to determine the weight configuration automatically. Our experimental results show that our proposed DWNB outperforms NB and its extensions significantly in multi-label classification.
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Kushiyama and Matsuoka. "Time Series GIS Map Dataset of Demolished Buildings in Mashiki Town after the 2016 Kumamoto, Japan Earthquake." Remote Sensing 11, no. 19 (September 20, 2019): 2190. http://dx.doi.org/10.3390/rs11192190.
Анотація:
After a large-scale disaster, many damaged buildings are demolished and treated as disaster waste. Though the weight of disaster waste was estimated two months after the 2016 earthquake in Kumamoto, Japan, the estimated weight was significantly different from the result when the disaster waste disposal was completed in March 2018. The amount of disaster waste generated is able to be estimated by an equation by multiplying the total number of severely damaged and partially damaged buildings by the coefficient of generated weight per building. We suppose that the amount of disaster waste would be affected by the conditions of demolished buildings, namely, the areas and typologies of building structures, but this has not yet been clarified. Therefore, in this study, we aimed to use geographic information system (GIS) map data to create a time series GIS map dataset with labels of demolished and remaining buildings in Mashiki town for the two-year period prior to the completion of the disaster waste disposal. We used OpenStreetMap (OSM) data as the base data and time series SPOT images observed in the two years following the Kumamoto earthquake to label all demolished and remaining buildings in the GIS map dataset. To effectively label the approximately 16,000 buildings in Mashiki town, we calculated an indicator that shows the possibility of the buildings to be classified as the remaining and demolished buildings from a change of brightness in SPOT images. We classified 5701 demolished buildings from 16,106 buildings, as of March 2018, by visual interpretation of the SPOT and Pleiades images with reference to this indicator. We verified that the number of demolished buildings was almost the same as the number reported by Mashiki municipality. Moreover, we assessed the accuracy of our proposed method: The F-measure was higher than 0.9 using the training dataset, which was verified by a field survey and visual interpretation, and included the labels of the 55 demolished and 55 remaining buildings. We also assessed the accuracy of the proposed method by applying it to all the labels in the OSM dataset, but the F-measure was 0.579. If we applied test data including balanced labels of the other 100 demolished and 100 remaining buildings, which were other than the training data, the F-measure was 0.790 calculated from the SPOT image of 25 March 2018. Our proposed method performed better for the balanced classification but not for imbalanced classification. We studied the examples of image characteristics of correct and incorrect estimation by thresholding the indicator.
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Hall, Imogen, Alex Pinto, Sharon Evans, Anne Daly, Catherine Ashmore, Suzanne Ford, Sharon Buckley, and Anita MacDonald. "The Challenges and Dilemmas of Interpreting Protein Labelling of Prepackaged Foods Encountered by the PKU Community." Nutrients 14, no. 7 (March 24, 2022): 1355. http://dx.doi.org/10.3390/nu14071355.
Анотація:
Phenylketonuria (PKU) can lead to severe intellectual impairment unless a phenylalanine-restricted diet starts early in life. It requires expert user knowledge about the protein content of foods. The ability of adults or caregivers of children with PKU to calculate protein exchanges from food labels on manufactured foods and any difficulties they encounter in interpreting food labels has not been studied systematically. Individuals with PKU or their caregivers residing in the UK were invited to complete a cross-sectional online survey that collected both qualitative and quantitative data about their experience when calculating protein exchanges from the food labelling on prepackaged foods. Data was available from 246 questionnaire respondents (152 caregivers of patients with PKU aged <18 years, 57 patients with PKU aged ≥18 years or their caregivers (n = 28), and 9 teenagers with PKU). Thirty-one per cent (n = 76/246) found it difficult to interpret food protein exchanges from food labels. The respondents listed that the main issues with protein labelling were the non-specification of whether the protein content was for the cooked or uncooked weight (64%, n = 158/246); labels stating foods contained 0 g protein but then included protein sources in the list of ingredients (56%, n = 137/246); the protein content being given after a product was prepared with regular milk rather than the dry weight of the product (55%, n = 135/246); and the non-clarity of whether the protein content was for the weight of prepared or unprepared food (in addition to non-specification of cooked or uncooked weights on food labelling) (54%, n = 133/246). Over 90% (n = 222/246) of respondents had experienced problems with food labelling in the previous six months. Misleading or confusing protein labelling of manufactured foods was common. The food industry and legislators have a duty to provide accurate and clear protein food labelling to protect populations requiring low protein diets.
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Enax, Laura, Ian Krajbich, and Bernd Weber. "Salient nutrition labels increase the integration of health attributes in food decision-making." Judgment and Decision Making 11, no. 5 (September 2016): 460–71. http://dx.doi.org/10.1017/s1930297500004563.
Анотація:
AbstractEvery day, people struggle to make healthy eating decisions. Nutrition labels have been used to help people properly balance the tradeoff between healthiness and taste, but research suggests that these labels vary in their effectiveness. Here, we investigated the cognitive mechanism underlying value-based decisions with nutrition labels as modulators of value.More specifically, we used a binary decision task between products along with two different nutrition labels to examine how salient, color-coded labels, compared to purely information-based labels, alter the choice process. Using drift-diffusion modeling, we investigated whether color-coded labels alter the valuation process, or whether they induce a simple stimulus-response association consistent with the traffic-light colors irrespective of the features of the item, which would manifest in a starting point bias in the model. We show that color-coded labels significantly increased healthy choices by increasing the rate of preference formation (drift rate) towards healthier options without altering the starting point. Salient labels increased the sensitivity to health and decreased the weight on taste, indicating that the integration of health and taste attributes during the choice process is sensitive to how information is displayed. Salient labels proved to be more effective in altering the valuation process towards healthier, goal-directed decisions.
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Sugeng, K. A., Z. Z. Barack, N. Hinding, and R. Simanjuntak. "Modular Irregular Labeling on Double-Star and Friendship Graphs." Journal of Mathematics 2021 (December 28, 2021): 1–6. http://dx.doi.org/10.1155/2021/4746609.
Анотація:
A modular irregular graph is a graph that admits a modular irregular labeling. A modular irregular labeling of a graph G of order n is a mapping of the set of edges of the graph to 1,2 , … , k such that the weights of all vertices are different. The vertex weight is the sum of its incident edge labels, and all vertex weights are calculated with the sum modulo n . The modular irregularity strength is the minimum largest edge label such that a modular irregular labeling can be done. In this paper, we construct a modular irregular labeling of two classes of graphs that are biregular; in this case, the regular double-star graph and friendship graph classes are chosen. Since the modular irregularity strength of the friendship graph also holds the minimal irregularity strength, then the labeling is also an irregular labeling with the same strength as the modular case.
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Zhang, Xinyu, Meng Kang, and Shuai Lü. "Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16881–89. http://dx.doi.org/10.1609/aaai.v38i15.29630.
Анотація:
Recently, instance contrastive learning achieves good results in unsupervised domain adaptation. It reduces the distances between positive samples and the anchor, increases the distances between negative samples and the anchor, and learns discriminative feature representations for target samples. However, most recent methods for identifying positive and negative samples are based on whether the pseudo-labels of samples and the pseudo-label of the anchor correspond to the same class. Due to the lack of target labels, many uncertain data are mistakenly labeled during the training process, and many low training potential data are also utilized. To address these problems, we propose Low Category Uncertainty and High Training Potential Instance Learning for Unsupervised Domain Adaptation (LUHP). We first propose a weight to measure the category uncertainty of the target sample. We can effectively filter the samples near the decision boundary through category uncertainty thresholds which are calculated by weights. Then we propose a new loss to focus on samples with high training potential. Finally, for anchors with low category uncertainty, we propose a sample reuse strategy to make the model more robust. We demonstrate the effectiveness of LUHP by showing the results of four datasets widely used in unsupervised domain adaptation.
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Setyawan, Deddy, Anis Nur Afni, Rafiantika Megahnia Prihandini, Ermita Rizki Albirri, and Arika Indah Kristiana. "PEWARNAAN TITIK TOTAL SUPER ANTI-AJAIB LOKAL PADA GRAF PETERSEN DIPERUMUM P(n,k) DENGAN k=1,2." BAREKENG: Jurnal Ilmu Matematika dan Terapan 15, no. 4 (December 1, 2021): 651–58. http://dx.doi.org/10.30598/barekengvol15iss4pp651-658.
Анотація:
The local antimagic total vertex labeling of graph G is a labeling that every vertices and edges label by natural number from 1 to such that every two adjacent vertices has different weights, where is The sum of a vertex label and the labels of all edges that incident to the vertex. If the labeling start the smallest label from the vertex then the edge so that kind of coloring is called the local super antimagic total vertex labeling. That local super antimagic total vertex labeling induces vertex coloring of graph G where for vertex v, the weight w(v) is the color of v. The minimum number of colors that obtained by coloring that induces by local super antimagic total vertex labeling of G called the chromatic number of local super antimagic total vertex coloring of G, denoted by χlsat(G). In this paper, we consider the chromatic number of local super antimagic total vertex coloring of Generalized Petersen Graph P(n,k) for k=1, 2.
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Chen, Zhiqiang, Leelavathi Rajamanickam, Jianfang Cao, Aidi Zhao, and Xiaohui Hu. "A localization strategy combined with transfer learning for image annotation." PLOS ONE 16, no. 12 (December 8, 2021): e0260758. http://dx.doi.org/10.1371/journal.pone.0260758.
Анотація:
This study aims to solve the overfitting problem caused by insufficient labeled images in the automatic image annotation field. We propose a transfer learning model called CNN-2L that incorporates the label localization strategy described in this study. The model consists of an InceptionV3 network pretrained on the ImageNet dataset and a label localization algorithm. First, the pretrained InceptionV3 network extracts features from the target dataset that are used to train a specific classifier and fine-tune the entire network to obtain an optimal model. Then, the obtained model is used to derive the probabilities of the predicted labels. For this purpose, we introduce a squeeze and excitation (SE) module into the network architecture that augments the useful feature information, inhibits useless feature information, and conducts feature reweighting. Next, we perform label localization to obtain the label probabilities and determine the final label set for each image. During this process, the number of labels must be determined. The optimal K value is obtained experimentally and used to determine the number of predicted labels, thereby solving the empty label set problem that occurs when the predicted label values of images are below a fixed threshold. Experiments on the Corel5k multilabel image dataset verify that CNN-2L improves the labeling precision by 18% and 15% compared with the traditional multiple-Bernoulli relevance model (MBRM) and joint equal contribution (JEC) algorithms, respectively, and it improves the recall by 6% compared with JEC. Additionally, it improves the precision by 20% and 11% compared with the deep learning methods Weight-KNN and adaptive hypergraph learning (AHL), respectively. Although CNN-2L fails to improve the recall compared with the semantic extension model (SEM), it improves the comprehensive index of the F1 value by 1%. The experimental results reveal that the proposed transfer learning model based on a label localization strategy is effective for automatic image annotation and substantially boosts the multilabel image annotation performance.
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Virk, Abaid ur Rehman, and A. Riasat. "Odd Graceful Labeling of W -Tree W T ( n , k ) and its Disjoint Union." Utilitas Mathematica 118 (January 8, 2024): 51–62. http://dx.doi.org/10.61091/um118-05.
Анотація:
Let G=(V(G),E(G)) be a graph with p vertices and q edges. A graph G of size q is said to be odd graceful if there exists an injection λ:V(G)→0,1,2,…,2q−1 such that assigning each edge xy the label or weight |λ(x)–λ(y)| results in the set of edge labels being 1,3,5,…,2q−1. This concept was introduced in 1991 by Gananajothi. In this paper, we examine the odd graceful labeling of the W-tree, denoted as WT(n,k).
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Saldanha, Leila G., Johanna T. Dwyer, Richard A. Bailen, Karen W. Andrews, Joseph W. Betz, Hua F. Chang, Rebecca B. Costello, et al. "Characteristics and Challenges of Dietary Supplement Databases Derived from Label Information." Journal of Nutrition 148, suppl_2 (August 1, 2018): 1422S—1427S. http://dx.doi.org/10.1093/jn/nxy103.
Анотація:
Abstract Launched in 2008, the Dietary Supplement Label Database (DSLD) permits the search of any term that appears anywhere on product labels. Since then, the database's search and download features have been periodically improved to enhance use for researchers and consumers. In this review, we describe how to customize searches and identify products and ingredients of interest to users in the DSLD, and provide the limitations of working with information derived from dietary supplement product labels. This article describes how data derived from information printed on product labels are entered and organized in the DSLD. Among the challenges are determining the chemical forms, types of extract, and amounts of dietary ingredients, especially when these are components of proprietary blends. The FDA announced new dietary supplement labeling regulations in May 2016. The 2017 DSLD has been updated to reflect them. These new regulations and examples cited in this article refer to this redesigned version of the DSLD. Search selection characteristics such as for product type and intended user group are as described in FDA guidance and regulations for dietary supplements. For this reason, some age groups (such as teens and seniors) and marketing recommendations for use (e.g., weight loss, performance, and other disease- or condition-specific claims) are not included in the search selections. The DSLD user interface features will be revised periodically to reflect regulatory and technologic developments to enhance user experience. A comprehensive database derived from analytically verified data on composition would be preferable to label data, but is not feasible for technical, logistic, and financial reasons. Therefore, a database derived from information printed on product labels is the only practical option at present for researchers, clinicians, and consumers interested in the composition of these products.
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Ratnasari, Lucia, Sri Wahyuni, Yeni Susanti, and Diah Junia Eksi Palupi. "Total edge irregularity strength of quadruplet and quintuplet book graphs." ITM Web of Conferences 36 (2021): 03004. http://dx.doi.org/10.1051/itmconf/20213603004.
Анотація:
Let G= (V, E) be a finite, simple and undirected graph with a vertex set V and an edge set E. An edge irregular total k-labelling is a function f : V ᴗE → {1,2,…,k} such that for any two different edges xy and x’y’ in E, their weights are distinct. The weight of edge xy is the sum of label of edge xy, labels of vertex x and of vertex y. The minimum k for which the graph G admits an edge irregular total k-labelling is called the total edge irregularity strength of G, denoted by tes(G). We have determined the total edge irregularity strength of book graphs, double book graphs and triple book graphs. In this paper, we show the exact value of the total edge irregularity strength of quadruplet book graphs and quintuplet book graphs.
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Bowness, J. Michael. "Cartilage fucoproteins with sites for cross-linking by transglutaminase." Biochemistry and Cell Biology 65, no. 4 (April 1, 1987): 280–85. http://dx.doi.org/10.1139/o87-036.
Анотація:
Slices of various types of cartilage were incubated with either L-[6-3H]fucose or [1,4-3H(N)]putrescine. Homogenization of the slices and fractionation of the homogenates showed for both labels that an insoluble collagenase-resistant fraction had the highest specific activity (dpm/mg dry weight). Examination of an exhaustive proteolytic digest of this insoluble fraction by ion-exchange high performance liquid chromatography showed the presence of γ-glutamyl[3H]putrescine. Chromatography of solubilized [3H]fucoprotein fractions showed the presence of several low molecular weight peaks, as well as high molecular weight material. Incubation of [3H]fucoprotein extracts with transglutaminase increased the high molecular weight peaks and decreased the low molecular weight ones. Incubation of the cartilage slices with L-[3H]fucose plus 0.5 mM dansylcadaverine, an inhibitor of transglutaminase, caused a decrease in the insoluble and high molecular weight fraction relative to the low molecular weight peaks. It is hypothesized that this is due to inhibition of cross-link formation between fucoprotein components of the cartilage which are transglutaminase substrates. One major low molecular weight peak, which labels with both fucose and putrescine, corresponds in size with the 15 000 subunit of collagen III aminopropeptide, which is known to be a substrate for transglutaminase.
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Putra, Ondra Eka. "Application of Fuzzy Logic in Making Automatic Labeling Stamping." SinkrOn 4, no. 1 (October 8, 2019): 155. http://dx.doi.org/10.33395/sinkron.v4i1.10166.
Анотація:
This research was conducted to create a labeling system on cardboard boxes with automatic stamping. Labeling is done based on height and weight of the cardboard so that stamping will be done according to the size and weight of the cardboard. The height of the cardboard is detected using an ultrasonic sensor and the weight of the cardboard is detected using a load cell sensor, and a stepper motor to move the cardboard label stamping. This research is based on artificial intelligence using mamdani method with fuzzy logic, so that all data can be processed properly. The height of the cardboard detected by the ultrasonic sensor is given a distance value of 10 cm to 45 cm with a value of rendah, sedang and tinggi. Cardboard weight detected by load cell sensor is given a weight value of 500 gr up to 1,500 gr with ringan, sedang and berat values. Stamping labels are driven by stepper motors which are given time values of 5 s to 15 s with time values of sebentar, sedang, and lama After the ultrasonic sensor and the load cell detect the cardboard box, the conveyor belt moves to run the cardboard box by the power window motor, then if the cardboard box passes through the proximity sensor, the conveyor belt stops and the stepper motor moves to stamping the cardboard label box automatically. The label stamping system is controlled by using the Arduino Mega 2560 to work well.
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Veldhuis, Jolanda, Elly A. Konijn, and Jacob C. Seidell. "Weight Information Labels on Media Models Reduce Body Dissatisfaction in Adolescent Girls." Journal of Adolescent Health 50, no. 6 (June 2012): 600–606. http://dx.doi.org/10.1016/j.jadohealth.2011.10.249.
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Wang, Chenxi, M. J. A. Khan, M. Ibrahim, E. Bonyah, M. K. Siddiqui, and S. Khalid. "On Edge Irregular Reflexive Labeling for Generalized Prism." Journal of Mathematics 2022 (March 7, 2022): 1–7. http://dx.doi.org/10.1155/2022/2886555.
Анотація:
Among the various ideas that appear while studying graph theory, which has gained much attraction especially in graph labeling, labeling of graphs gives mathematical models which value for a vast range of applications in high technology (data security, cryptography, various problems of coding theory, astronomy, data security, telecommunication networks, etc.). A graph label is a designation of graph elements, i.e., the edges and/or vertex of a group of numbers (natural numbers), and is called assignment or labeling. The vertex or edge labeling is related to their domain asset of vertices or edges. Likewise, for total labeling, we take the domain as vertices and edges both at the same time. The reflexive edge irregularity strength (res) is total labeling in which weights of edges are not the same for all edges and the weight of an edge is taken as the sum of the edge labels and the vertices associated with that edge. In the res, the vertices are labeled with nonnegative even integers while the edges are labeled with positive integers. We have to make the labels minimum, whether they are associated with vertices or edges. If such labeling exists, then it is called the res of H and is represented as s res H . In this paper, we have computed the res for the Cartesian product of path and cycle graph which is also known as generalizing prism.
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Han, Haoyu, Tsz Tung Wong, Senhei He, Muyin Cai, and Shile Lei. "In-Depth exploration and potential improvements on learning fair classifiers with partially annotated datasets." Applied and Computational Engineering 73, no. 1 (July 5, 2024): 179–86. http://dx.doi.org/10.54254/2755-2721/73/20240392.
Анотація:
In recent years, fairness-aware learning has been increasingly investigated. Researchers are trying to train accurate but fair classifiers. Yet, most existing methods rely on a fully- annotated dataset, which is an unrealistic assumption, since majority of the sensitive attributes of data remained unlabelled. This paper thoroughly explores this problem, namely Fairness - Aware Learning on Partially Labeled Datasets (FAL-PL) and Confidence-based Group Label Assignment (CGL), which is an innovative attempt to address FAL-PL. We conduct experiments by altering the hyperparameter, epoch, and the parameter, group-label ratio of CGL and discover that this methods results are easily affected by slight changes in the epoch and group-label ratio. Such unstableness reveals CGLs lack of robustness. We propose 2 modifications to further enhance CGL 1. Co- teaching Method for Classifier Training: We use the co-teaching method, which employs two models for training. We create these models by tweaking parameters and epochs in the original CGL model. After training, we choose the better-performing classifier based on accuracy. 2. Reducing Impact of False Pseudo Labels: We've noticed an issue with the CGL method random false label assignments can lead to errors. When two outcomes have similar probabilities, CGL might assign the wrong label. To address this, we propose a new parameter, w, based on Gini impurity. It measures similarity between probabilities and acts as a weight, minimizing the influence of unreliable labels during the training stage of final fair modelf.