Label Information Research Articles

Design and implementation of dynamic balance in ultra-high speed IP network

Abstract Nowadays, with the rapid development of IP network service demand, network construction is also accelerating, IP network is expanding at a higher rate, and gradually moving toward ultra-high speed networks above 40G. In view of the problems of inconsistent IP packet order, random IP packet length and traffic burst brought by the application environment of high traffic, high bandwidth and high concurrency in ultra-high speed IP network, this paper introduces a kind of dynamic balance processing technology of ultra-high speed IP network based on information label. By optimizing the IP processing architecture, a two-level scheduling architecture of multi-board and multi-module is established, and VPN is established in ultra-high speed IP network by using dynamic balance processing technology and order preserving technology to realize high performance IP processing, so as to achieve ultra-high speed data transmission.

Learning Accurate Label-Specific Features From Partially Multilabeled Data.

Feature selection is an effective dimensionality reduction technique, which can speed up an algorithm and improve model performance such as predictive accuracy and result comprehensibility. The study of selecting label-specific features for each class label has attracted considerable attention since each class label might be determined by some inherent characteristics, where precise label information is required to guide label-specific feature selection. However, obtaining noise-free labels is quite difficult and impractical. In reality, each instance is often annotated by a candidate label set that comprises multiple ground-truth labels and other false-positive labels, termed partial multilabel (PML) learning scenario. Here, false-positive labels concealed in a candidate label set might induce the selection of false label-specific features while masking the intrinsic label correlations, which misleads the selection of relevant features and compromises the selection performance. To address this issue, a novel two-stage partial multilabel feature selection (PMLFS) approach is proposed, which elicits credible labels to guide accurate label-specific feature selection. First, the label confidence matrix is learned to help elicit ground-truth labels from the candidate label set via the label structure reconstruction strategy, each element of which indicates how likely a class label is ground truth. After that, based on distilled credible labels, a joint selection model, including label-specific feature learner and common feature learner, is designed to learn accurate label-specific features to each class label and common features for all class labels. Besides, label correlations are fused into the features selection process to facilitate the generation of an optimal feature subset. Extensive experimental results clearly validate the superiority of the proposed approach.

Flow cytometric analysis of the murine placenta to evaluate nanoparticle platforms during pregnancy

Clinically approved therapeutics for obstetric conditions are extremely limited, with over 80% of drugs lacking appropriate labeling information for pregnant individuals. The pathology for many of these obstetric conditions can be linked to the placenta, necessitating the development of therapeutic platforms for selective drug delivery to the placenta. When evaluating therapeutics for placental delivery, literature has focused on ex vivo delivery to human placental cells and tissue, which can be difficult to source for non-clinical researchers. Evaluating in vivo drug delivery to the placenta using small animal models can be more accessible than using human tissue, but robust, quantitative methods to characterize delivery remain poorly established. Here, we report a flow cytometric method to evaluate in vivo drug delivery to the murine placenta. Specifically, we describe techniques to identify key cell types in the murine placenta — trophoblasts, endothelial cells, and immune cells — via flow cytometric analysis. While we have employed this method to detect lipid nanoparticle-mediated nucleic acid delivery, this approach can extend to a variety of drug carriers (e.g., liposomes, exosomes, polymeric and metallic nanoparticles) and payloads (e.g., small molecules, proteins, other nucleic acids). Similarly, we describe the application of this method toward immunophenotypic analysis to assess changes in the placental immune environment during disease or in response to a therapeutic. Together, the techniques reported herein aim to broaden the accessibility of placental research in an effort to encourage collaboration between physician-scientists, engineers, placental biologists, and clinicians for developing novel therapeutics to treat placental conditions during pregnancy.

Encapsulating Fe3O4 Nanoparticles and Carbon Dots in a Metal-Organic Framework for Magnetic Fluorescent Taggants.

Magnetic fluorescent composite nanomaterials have broad application prospects in the fields of biological imaging, anticounterfeiting identification, suspicious object tracking, and identification of latent fingerprints in forensic medicine. For an effective taggant, a clearly visible identifying mark is necessary to enable observers to capture labeling information quickly and accurately, even from a distance. The preparation method of magnetic fluorescent composite materials is complicated and usually needs different surface modification and assembly processes. The limited loading capacity of fluorescent materials also limits the fluorescence properties of the composite, so it is difficult to produce obvious fluorescence as a taggant to meet the requirements of visible labeling. In this study, a core-shell structure of a magnetic fluorescent composite was prepared by using the metal-organic framework ZIF-8 as the host of fluorescent materials and an encapsulation shell coated on the Fe3O4 nanoparticles. The porous ZIF-8 is beneficial for increasing the loading capacity of fluorescent materials to ensure the fluorescence performance of the composite materials. Further modification of the composite surface prevented the desorption of fluorescent materials from the pores of ZIF-8, enabling the samples to maintain good fluorescence properties even after multiple washing cycles. The preparation method is simple, rapid, and cost-effective, and the prepared magnetic fluorescent composite nanomaterial has high magnetic separation performance and fluorescence performance, making it a promising material for identification, marking, and tracking.

Deep Double Incomplete Multi-View Multi-Label Learning With Incomplete Labels and Missing Views.

View missing and label missing are two challenging problems in the applications of multi-view multi-label classification scenery. In the past years, many efforts have been made to address the incomplete multi-view learning or incomplete multi-label learning problem. However, few works can simultaneously handle the challenging case with both the incomplete issues. In this article, we propose a new incomplete multi-view multi-label learning network to address this challenging issue. The proposed method is composed of four major parts: view-specific deep feature extraction network, weighted representation fusion module, classification module, and view-specific deep decoder network. By, respectively, integrating the view missing information and label missing information into the weighted fusion module and classification module, the proposed method can effectively reduce the negative influence caused by two such incomplete issues and sufficiently explore the available data and label information to obtain the most discriminative feature extractor and classifier. Furthermore, our method can be trained in both supervised and semi-supervised manners, which has important implications for flexible deployment. Experimental results on five benchmarks in supervised and semi-supervised cases demonstrate that the proposed method can greatly enhance the classification performance on the difficult incomplete multi-view multi-label classification tasks with missing labels and missing views.

Knowledge, perception, and practical understanding of food labels: A cross-sectional study among Bangladeshi consumers.

This research aimed to assess consumers' knowledge, perception, and practical understanding of food labels. A validated, structured questionnaire was employed for data collection. Data were collected from 1238 respondents covering all eight administrative divisions of Bangladesh using a nonrandomized convenience sampling method. Linear regression analyses were conducted to establish the relationship between demographic attributes and respondents' practical understanding of food labels. The majority of participants (52.5%) actively read food labels when purchasing a product for the first time. Food labels are regarded as "very important" by 56.2% of respondents and "important" by 35.7% of respondents. Label information is prioritized to highlight the importance of clear production and expiration dates (70%), followed by nutritional composition (56.7%) and source of raw material (52.5%). Despite this, over half of the participants demonstrated a limited practical understanding of the nutritional components on labels. The results of our linear regression analysis suggest that individuals within the age range of 18-28, belonging to Muslim communities, residing in the Dhaka and Khulna divisions, being higher educated, possessing a good understanding of nutrition, and being acquainted with the food safety agency of Bangladesh tend to exhibit a greater degree of practical knowledge regarding food labeling when compared to their counterparts. The study emphasizes the importance of clearer label information, particularly for nutritional value, and calls for targeted educational programs to improve consumer understanding of food labels, with a focus on older age groups and expanded educational efforts.

Improving consumer understanding of pesticide toxicity labels: experimental evidence

Consumers often inadvertently misperceive the health hazards associated with over-the-counter pesticides under the current textual labeling policy, potentially leading to improper use. We conducted an incentivized framed field experiment with eye tracking to evaluate the effectiveness of the current pesticide labels that convey risk using signal words (Caution, Warning, Danger) compared to two visually focused label alternatives: traffic light colors and skull intensity symbols. A total of 166 participants were randomly assigned to one of three label formats and asked to rank toxicity levels and make purchasing decisions within multiple price lists. Results show that signal words fail to adequately communicate toxicity levels. Specifically, participants’ correct assessment of toxicity level dramatically improves from 54% under the existing signal word label to 95% under the traffic light and 83% under the skull intensity symbol labels. We also find that participants are more likely to choose the less toxic alternatives under the new labels, suggesting the current labeling system may affect choice and have unintended adverse effects on human health.

SGCL: Semi-supervised Graph Contrastive Learning with confidence propagation algorithm for node classification

Semi-Supervised Graph Learning (SSGL) aims to predict massive unknown labels based on a subset of known labels within a graph. Recently, graph neural network, one of the most popular SSGL approaches, has garnered considerable research interest and achieved remarkable progress. However, many of these methods perform unsatisfactorily with limited labeled data. Graph contrastive learning (GCL), which utilizes unlabeled data to generate supervision, partially addresses this issue but does not fully exploit label information. To address this challenge, we propose SSGL algorithm, Semi-supervised Graph Contrastive Learning with Confidence Propagation Algorithm (SGCL). SGCL comprises two stages of contrastive learning. In the first stage, we employ unsupervised contrastive learning to initialize the model with graph augmentation. In the second stage, in order to fully leverage known labels and graph structure, we incorporate supervised contrastive learning which utilizes supervision signals obtained from confidence propagation algorithm. By combining supervised contrastive learning and unsupervised contrastive learning, the embedding quality and the classification accuracy can be further enhanced. At last, comprehensive experiments demonstrate that SGCL outperforms the best baseline method by an average of 2.23% across six datasets, highlighting the effectiveness of our approach.

Wear state identification of ball screw meta action unit based on parameter optimization VMD and improved Bilstm

In this paper, a novel neural network based on parameter optimization Variational Mode Decomposition (VMD) and improved bidirectional long short-term memory (BiLSTM) is proposed. Wear state recognition method of ball screw element action unit component based on Bilstm. Firstly, Tent chaotic map and adaptive sine cosine Algorithm were used to improve the improved Northern Goshawk Optimisation Algorithm (INGO) to verify the superiority of INGO algorithm and determine the optimal parameter combination of VMD. Secondly, INGO-VMD was used to decompose the collected vibration signals and calculate the correlation of IMF components, and the multi-feature information matrix that could characterize the wear state change of the lead screw was constructed after retaining the IMF components with large correlation. Finally, the divided feature information matrix and labels were input into the Bilstm network model of Bayesian optimization (BO) for training, and the Softmax classifier was used to classify and identify the wear state category.

Cannabinoid Content and Label Accuracy of Various Hemp-Derived Haircare, Cosmetic, and Edible Products Available at Retail Stores and Online in the United States.

Aim: To evaluate the label accuracy and content of various hemp-derived cannabidiol (CBD) products (cannabinoid products with ≤0.3% Δ9-tetrahydrocannabinol [THC]), as well as evaluate advertised claims on product labels. Methods: Hemp haircare, cosmetics, and food/drink products that were advertised to contain CBD were purchased from retail stores in the Baltimore, Maryland area (purchased in July 2020) and online (purchased in August 2020). Cannabinoid concentrations were measured using gas chromatography-mass spectrometry. Percent deviations between labeled and actual CBD concentrations were determined. Label information such as references to the Food and Drug Administration (FDA), external testing claims, and other claims (i.e., cosmetic or beauty, therapeutic, health halo effect, or "other") were quantified. Results: Ninety-seven products were purchased (35 in-store, 62 online). Of the 71 products with a specific total CBD amount on the label, 35 (49%) were underlabeled (>10% more CBD than advertised), 27 (38%) were overlabeled (>10% less CBD than advertised), and 9 (12.7%) were accurately labeled (within ±10% of labeled CBD). The median (range) percentage deviations were -53% (-100%-76%) for haircare products, +18% (-100%-1076%) for cosmetics, and -1% (-100%-4468%) for food/drinks. CBD label accuracy did not differ significantly between products with external testing claims versus those without (t40 = 0.23, p = 0.82). Overall, 24% of the 97 (total) products made a cosmetic or beauty claim (e.g., "skin looks more youthful"), 40% made a therapeutic claim (e.g., "pain relief"), and 86% made a health halo effect claim (e.g., "paraben-free," "dye-free," etc.). Most products (63%) did not include a disclaimer that claims had not been evaluated by the FDA. Conclusions: Most of the products included in this sample were inaccurately labeled for CBD content, including those claiming to have been tested by third party laboratories. A notable finding was that 10 products did not contain any CBD. Many products made therapeutic claims or used marketing tactics to seemingly convey they were safe/healthy, but only about one-third included disclaimers that these statements had not been evaluated by the FDA. These findings highlight the need for proper regulatory oversight of cannabinoid-containing products to ensure quality assurance and deter misleading or unfounded health claims in product marketing.

Discriminative Feature Enhancement Network for few-shot classification and beyond

Few-shot classification aims to recognize query samples from novel classes given scarce labeled data, which remains a challenging problem in machine learning. This paper proposes a Discriminative Feature Enhancement Network (DFENet) to distinguish the discriminative feature of a novel category with the following characteristics: (1) a Cross-Modal Guidance Module (CMG-Module) is proposed to enrich the query vectors via leveraging label information from the additional modality; (2) a Neural-Decoding based Attention Module (NDA-Module) further explores the relationship between the query and support samples, and the attention weights depend on contribution of the support to the reconstructed query. The main intuition of the NDA-Module is to deepen our understanding of the self-attention mechanism from a neural decoding perspective, emphasizing that the best reconstruction can be used as a universal guideline; (3) a flexible triplet loss is designed to incorporate the semantic context among all classes and distinguish samples from different classes. The experimental results on two few-shot classification datasets show that our DFENet can learn a more discriminative representation for novel classes progressively. We also test the proposed method on image retrieval and facial expression recognition tasks, demonstrating consistent improvement.

Think and Choose! The Dual Impact of Label Information and Consumer Attitudes on the Choice of a Plant-Based Analog.

This study explored the impact of various label information (extrinsic attributes) and sociodemographic and attitudinal factors (intrinsic attributes) on Brazilian consumer choices, using simulated traditional and plant-based muçarela cheese as the model product. The research was conducted in two phases: the first involved a structured questionnaire assessing attitudinal dimensions such as Health Consciousness, Climate Change, Plant-based Diets, and Food Neophobia, along with sociodemographic data collection. The second phase comprised a discrete choice experiment with (n = 52) and without (n = 509) eye tracking. The term "Cheese" on labels increased choice probability by 7.6% in a general survey and 15.1% in an eye tracking study. A prolonged gaze at "Cheese" did not affect choice, while more views of "Plant-based product" slightly raised choice likelihood by 2.5%. Repeatedly revisiting these terms reduced the choice probability by 3.7% for "Cheese" and 1% for "Plant-based product". Nutritional claims like "Source of Vitamins B6 and B12" and "Source of Proteins and Calcium" boosted choice probabilities by 4.97% and 5.69% in the general and 8.4% and 6.9% in the eye-tracking experiment, respectively. Conversely, front-of-package labeling indicating high undesirable nutrient content decreased choice by 13% for magnifying presentations and 15.6% for text. In a plant-based subsample, higher environmental concerns and openness to plant-based diets increased choice probabilities by 5.31% and 5.1%, respectively. These results highlight the complex dynamics between label information, consumer understanding, and decision-making.

The taxonomic composition and chronology of a museum collection of Coleoptera revealed through large-scale digitisation

IntroductionHistoric museum collections hold a wealth of biodiversity data that are essential to our understanding of the rapidly changing natural world. Novel curatorial practices are needed to extract and digitise these data, especially for the innumerable pinned insects whose collecting information is held on small labels.MethodsWe piloted semi-automated specimen imaging and digitisation of specimen labels for a collection of ~29,000 pinned insects of ground beetles (Carabidae: Lebiinae) held at the Natural History Museum, London. Raw transcription data were curated against literature sources and non-digital collection records. The primary data were subjected to statistical analyses to infer trends in collection activities and descriptive taxonomy over the past two centuries.ResultsThis work produced research-ready digitised records for 2,546 species (40% of known species of Lebiinae). Label information was available on geography in 91% of identified specimens, and the time of collection in 39.8% of specimens and could be approximated for nearly all specimens. Label data revealed the great age of this collection (average age 91.4 years) and the peak period of specimen acquisition between 1880 and 1930, with little differences among continents. Specimen acquisition declined greatly after about 1950. Early detected species generally were present in numerous specimens but were missing records from recent decades, while more recently acquired species (after 1950) were represented mostly by singleton specimens only. The slowing collection growth was mirrored by the decreasing rate of species description, which was affected by huge time lags of several decades to formal description after the initial specimen acquisition.DiscussionHistoric label information provides a unique resource for assessing the state of biodiversity backwards to pre-industrial times. Many species held in historical collections especially from tropical super-diverse areas may not be discovered ever again, and if they do, their recognition requires access to digital resources and more complete levels of species description. A final challenge is to link the historical specimens to contemporary collections that are mostly conducted with mechanical trapping of specimens and DNA-based species recognition.

Securing Anomaly Detection for Process-Based Time Series

Widespread innovation from artificial intelligence and machine learning (AI/ML) tools presents a lucrative opportunity for the nuclear industry to improve state-of-the-art analyses (e.g. condition monitoring, remote operation, etc.) due to increased data visibility. In recent years, the risk posed by collaborative data exchange has received increased attention due, in part, to a potential adversary’s ability to reverse-engineer intercepted data using domain knowledge and AI/ML tools. While the efficacy of typical encryption has been proven during passive communication and data storage, collaborative exchange typically requires decryption for extended analyses by a third-party, which poses an intrinsic risk due to these trustworthiness concerns. The directed infusion of data (DIOD) 1 1 A.Al Rashdan and H.Abdel-Khalik, Deceptive Infusion of Data, Non-Provisional Patent, Application No. 63/227,389, September 2022. paradigm presented in this paper discusses a novel data masking technique that relies on preserving the usable information of proprietary data while concealing its identity via reduced-order modeling. In contrast to existing state-of-the-art data masking methods, DIOD does not impose limiting assumptions, computational overheads, or induced uncertainties, thereby allowing for secure and flexible data-level security that does not alter the inferential content of the data. This paper focuses on the application of DIOD to a process-based simulation wherein a leaking reservoir with a controlled inlet pump is simulated under various experimental conditions with the goal of producing masked data that preserve the information given by anomalies. These experiments included the injection of statistically significant anomalies, subtle anomalies that occurred over an extended period, and the addition of several independent anomalous states. Each experiment showed that a classifier will identify the same anomalies whether it analyzes the original or masked data. An additional experiment also tested the case of corrupted labeling information wherein labels were arbitrarily randomized, and the loss in labeling accuracy was about the same for both datasets. Each of these experiments show that data obfuscated by DIOD may be utilized in the place of real data for a variety of condition monitoring scenarios with no loss in performance.

Innovation of Processed Baronang Fish as One of The Local Food Products in Gebang Village, Pesawaran

Gebang Pesawaran Village is an area that occupies a coastal area and has quite abundant marine products, but the potential for utilization is less than optimal and there is no food made from any of the existing marine products. This research aims to explore the potential of rabbitfish itself and to create local food products that are losng-lasting but still nutritious in Gebang Pesawaran Village. It is also an effort to create a variety of food dishes that do not only depend on one type of ingredient, but also utilize a variety of food ingredients. The product made in this research was serundeng with coconut and rabbitfish as the basic ingredients, which was named Seronang. The method used in this research is qualitative with a descriptive approach. The results of the research show that the baronang fish serundeng product is a new food innovation in Gebang Village. This product has passed food chemical test analysis and has a nutritional value information label to make it easier for consumers to consume the product. From the results of chemical food tests, it is known that the nutritional content contained in baronang fish serundeng products includes air content of 5.4481%, glucose 6.1474%, fat 32.8965%, carbohydrates 31.8109%, protein 23.6970%, and vitamins 8.8597%.

Few-shot relational triple extraction with hierarchical prototype optimization

Relational Triple Extraction (RTE) aims to extract relations and entities from unstructured text. Current RTE models using supervised learning require a large amount of labeled data, which presents a challenge for real-world applications. Therefore, the research work on Few-Shot Relational Triple Extraction (FS-RTE) has been proposed. However, the existing work cannot effectively construct accurate prototypes from a small number of samples, and it is difficult to model the dependencies between entities and relations, resulting in poor performance in relational triple extraction. In this paper, we propose a Hierarchical Prototype Optimized FS-RTE method (HPO). In particular, to mitigate prototype bias built on a small number of samples, HPO uses prompt learning to merge the information of relational labels into the text. Then, the entity-level prototypes are constructed using a span encoder to avoid label dependency between entity tokens. Finally, the hierarchical contrastive learning (HCL) method is introduced to improve the metric space between the prototypes of entities and relations, respectively. Experiments conducted on two public datasets show that HPO can significantly outperform previous state-of-the-art methods.

Bug numbers matter: An empirical study of effort‐aware defect prediction using class labels versus bug numbers

AbstractPrevious research have utilized public software defect datasets such as NASA, RELINK, and SOFTLAB, which only contain class label information. Most effort‐aware defect prediction (EADP) studies are carried out around these datasets. However, EADP studies typically relying on predicted bug number (i.e., considering modules as effort) or density (i.e., considering lines of code as effort) for ranking software modules. To explore the impact of bug number information in constructing EADP models, we access the performance degradation of the best‐performing learning‐to‐rank methods when using class labels instead of bug numbers for training. The experimental results show that using class labels instead of bug numbers in building EADP models results in an decrease in the detected bugs when module is considering as effort. When effort is LOC, using class labels to construct EADP models can lead to a significant increase in the initial false alarms and a significant increase in the modules that need to be inspected. Therefore, we recommend not only the class labels but also the bug number information should be disclosed when publishing software defect datasets, in order to construct more accurate EADP models.

A new Ranunculus (Ranunculaceae) from Nevada, U.S.A.

A new species of Ranunculus from Elko County, Nevada, R. legerae, is described and illustrated. It is related to R. triternatus, R. glaberimmus, and R. adoneus. The earliest known collection was made in 1937. Poor label information prevented rediscovery, which did not happen until 2017. The second author discovered a large population in 2022 which led to this study and article.

Partial label feature selection via label disambiguation and neighborhood mutual information

Partial label learning aims to learn from training instances, each of which is associated with a set of candidate labels but only one is a ground-truth label. Feature selection is an effective method to improve the generalization capability of the learning model; however, partial label feature selection work is exceptionally challenging due to the limitation and ambiguity of label information. Therefore, this paper proposes a partial label feature selection algorithm based on label disambiguation and neighborhood mutual information. Firstly, neighborhood granularity is utilized to determine the neighborhoods of instances to disambiguate the candidate labels. Secondly, based on label confidence induced by disambiguation, feature relevance and redundancy are measured by neighborhood mutual information, which avoids the negative impact of data discretization on feature selection and directly handles continuous features. Concurrently, the kappa coefficient is employed to estimate the label consistency for describing the influences of feature changes on the label space. Then, the significance of each feature is evaluated by fusing feature relevance, feature redundancy, and label consistency. Finally, the effectiveness of the proposed algorithm is verified by comparing the proposed algorithm with four base classifiers and other feature selection methods. Furthermore, the feasibility of the proposed disambiguation method is demonstrated through comparison with four state-of-the-art disambiguation methods.

CentroIDA: Cross-domain class discrepancy minimization based on accumulative class-centroids for Imbalanced Domain Adaptation

Unsupervised Domain Adaptation (UDA) is a critical task in transfer learning, which seeks to apply the knowledge acquired in one domain (the source domain) to a different but related domain (the target domain). In this context, the two domains exhibit Out-Of-Distribution(OOD), and the label information of the target domain is unavailable. Although numerous UDA methods have made significant strides in mitigating the discrepancies in feature distributions across domains, they often neglect a more prevalent real-world scenario. This scenario, referred to as Imbalanced Domain Adaptation (IDA) task, is characterized by the coexistence of differences in both feature and label distributions. Our empirical analysis suggests that overlooking the disparities in label distributions can severely compromise the performance of the model, thereby limiting the generalizability of domain adaptation models. Consequently, our objective is to introduce a model capable of concurrently addressing differences in both feature and label distributions, thereby fostering a broader application of domain adaptation methodologies. In this paper, we propose a cross-domain class discrepancy minimization method based on accumulative class-centroids for IDA, termed as centroIDA. The strategies of accumulative class-centroids alignment and class-wise feature alignment pay heed to the relationship between cross-domain intra-class features and inter-class features, thereby facilitating conditional feature alignment to overcome both feature and label distribution differences simultaneously. Our experimental evaluation on OfficeHome and DomainNet datasets reveals that, in comparison to TIToK (a novel method also designed to tackle the IDA problem), our method enhances the average accuracy by 5.6% and 2.7%, respectively. Furthermore, as the label shift intensifies, our method consistently outperforms other methods, demonstrating a robust resistance to label shift.