Multi-level Label Research Articles

Hierarchical Progressive Image Forgery Detection and Localization Method Based on UNet

The rapid development of generative technologies has made the production of forged products easier, and AI-generated forged images are increasingly difficult to accurately detect, posing serious privacy risks and cognitive obstacles to individuals and society. Therefore, constructing an effective method that can accurately detect and locate forged regions has become an important task. This paper proposes a hierarchical and progressive forged image detection and localization method called HPUNet. This method assigns more reasonable hierarchical multi-level labels to the dataset as supervisory information at different levels, following cognitive laws. Secondly, multiple types of features are extracted from AI-generated images for detection and localization, and the detection and localization results are combined to enhance the task-relevant features. Subsequently, HPUNet expands the obtained image features into four different resolutions and performs detection and localization at different levels in a coarse-to-fine cognitive order. To address the limited feature field of view caused by inconsistent forgery sizes, we employ three sets of densely cross-connected hierarchical networks for sufficient interaction between feature images at different resolutions. Finally, a UNet network with a soft-threshold-constrained feature enhancement module is used to achieve detection and localization at different scales, and the reliance on a progressive mechanism establishes relationships between different branches. We use ACC and F1 as evaluation metrics, and extensive experiments on our method and the baseline methods demonstrate the effectiveness of our approach.

Willingness to pay for a multi-level animal husbandry label: an analysis of German meat consumers

PurposeThe use of multi-level labels can enhance product visibility by enabling labeling of various items. Moreover, it can better accommodate the diversity on both the producer and consumer sides. However, studies on the willingness to pay (WTP) for premium levels of those animal welfare labels are scarce.Design/methodology/approachWe investigate consumers’ WTP for a four-level animal husbandry label introduced to the market by German retailers in 2019 by conducting an online survey with 1,223 German meat consumers using Van Westendorp’s price sensitivity meter (PSM).FindingsThere is a significant increase in WTP for level 3 of the husbandry label, but only a slight increase for level 4. One explanation is that consumers may have the mistaken belief that level 3 already includes outdoor access for animals. As a result of this expectation, consumers may not perceive much added value in level 4, which is reflected in their reluctance to pay a higher price. This is reinforced by the finding that once informed of the criteria, 18% of the participants reduced their WTP for level 3, whereas only 6% considered a discount for level 4. Furthermore, 40% were prepared to pay more for level 4 after being informed of the respective criteria than they had previously stated.Originality/valueTo the best of our knowledge, this study is the first to analyze and emphasize the importance of clear label communication, particularly for multi-level animal husbandry labels.

Hierarchical-taxonomy-aware and attentional convolutional neural networks for acoustic identification of bird species: A phylogenetic perspective

The study of bird populations is crucial for biodiversity research and conservation. Deep artificial neural networks have revolutionized bird acoustic recognition; however, most methods overlook inherent relationships among bird populations, resulting in the loss of biological information. To address this limitation, we propose the Phylogenetic Perspective Neural Network (PPNN), which incorporates hierarchical multilevel labels for each bird. PPNN uses a hierarchical semantic embedding framework to capture feature information at different levels. Attention mechanisms are employed to extract and select common and distinguishing features, thereby improving classification accuracy. We also propose a path correction strategy to rectify inconsistent predictions. Experimental results on bird acoustic datasets demonstrate that PPNN outperforms current methods, achieving classification accuracies of 90.450%, 91.883%, and 89.950% on the Lishui-Zhejiang Birdsdata (100 species), BirdCLEF2018-Small (150 species), and BirdCLEF2018-Large (500 species) datasets respectively, with the lowest hierarchical distance of a mistake across all datasets. Our proposed method is applicable to any bird acoustic dataset and presents significant advantages as the number of categories increases.

Multi-Level Label Correction by Distilling Proximate Patterns for Semi-Supervised Semantic Segmentation

Multi-Level Label Correction by Distilling Proximate Patterns for Semi-Supervised Semantic Segmentation

Effects of Multi-Level Eco-Labels on the Product Evaluation of Meat and Meat Alternatives-A Discrete Choice Experiment.

Eco-labels are an instrument for enabling informed food choices and supporting a demand-sided change towards an urgently needed sustainable food system. Lately, novel eco-labels that depict a product's environmental life cycle assessment on a multi-level scale are being tested across Europe's retailers. This study elicits consumers' preferences and willingness to pay (WTP) for a multi-level eco-label. A Discrete Choice Experiment was conducted; a representative sample (n = 536) for the Austrian population was targeted via an online survey. Individual partworth utilities were estimated by means of the Hierarchical Bayes. The results show higher WTP for a positively evaluated multi-level label, revealing consumers' perceived benefits of colorful multi-level labels over binary black-and-white designs. Even a negatively evaluated multi-level label was associated with a higher WTP compared to one with no label, pointing towards the limited effectiveness of eco-labels. Respondents' preferences for eco-labels were independent from their subjective eco-label knowledge, health consciousness, and environmental concern. The attribute "protein source" was most important, and preference for an animal-based protein source (beef) was strongly correlated with consumers' meat attachment, implying that a shift towards more sustainable protein sources is challenging, and sustainability labels have only a small impact on the meat product choice of average consumers.

MLGAL: Multi-level Label Graph Adaptive Learning for node clustering in the attributed graph

Node clustering aims to divide nodes into disjoint groups. Recently, a considerable amount of research leverages Graph Neural Networks (GNNs) to learn compact node embeddings, which are then used as input of the traditional clustering methods to get better clustering results. While in most of these methods the node representation learning and the clustering are performed separately, a few works have further coupled them in a self-supervised learning manner. The coupling, however, should be carefully designed to avoid potential noises in the pseudo labels generated automatically during the training process.To address the above problems, in this article, we propose Multi-level Label Graph Adaptive Learning (MLGAL), a novel unsupervised learning algorithm for the node clustering problem. We first design a graph filter to smooth the node features. Then, we iteratively choose the similar and the dissimilar node pairs to perform the adaptive learning with the multi-level label, i.e., the node-level label and the cluster-level label generated automatically by our model. We conduct extensive experiments on four benchmark datasets to evaluate the performance of our model. The results demonstrate that our model outperforms the state-of-the-art baselines on four benchmark datasets.

A Label Dependence-Aware Sequence Generation Model for Multi-Level Implicit Discourse Relation Recognition

Implicit discourse relation recognition (IDRR) is a challenging but crucial task in discourse analysis. Most existing methods train multiple models to predict multi-level labels independently, while ignoring the dependence between hierarchically structured labels. In this paper, we consider multi-level IDRR as a conditional label sequence generation task and propose a Label Dependence-aware Sequence Generation Model (LDSGM) for it. Specifically, we first design a label attentive encoder to learn the global representation of an input instance and its level-specific contexts, where the label dependence is integrated to obtain better label embeddings. Then, we employ a label sequence decoder to output the predicted labels in a top-down manner, where the predicted higher-level labels are directly used to guide the label prediction at the current level. We further develop a mutual learning enhanced training method to exploit the label dependence in a bottom-up direction, which is captured by an auxiliary decoder introduced during training. Experimental results on the PDTB dataset show that our model achieves the state-of-the-art performance on multi-level IDRR. We release our code at https://github.com/nlpersECJTU/LDSGM.

A fast community detection algorithm using a local and multi-level label diffusion method in social networks

One of the popular categories of community detection methods are label propagation-based algorithms. Label propagation-based algorithms use local criteria and have a near-linear time complexity. However, these algorithms have problems such as low accuracy, instability, and high computational time in comparison with other local methods. This article presents a fast and simple label diffusion method (FSLD), using local criteria to discover communities accurately in large-scale networks. In FSLD method, community formation is initially started from a low-degree periphery node and then it diffuses its label from outer to inner side of community in a multi-level way. In next step, using a label updating step, all nodes from high-degree to low-degree have the potential to update and finalize their label to obtain initial communities. The experimental results reveal the higher accuracy and performance of the proposed FSLD algorithm in comparison to other state-of-the-art algorithms.

Big graph partitioning

At many levels, from storage infrastructures to programming paradigms, billion-node graphs pose considerable obstacles. It is necessary to provide a general-purpose graph-processing platform. A distributed memory system is thought to be a viable platform for both on-line and offline query processing and graph analytics. In this research, we look at how to partition a billion-node graph on such a platform, which is crucial because it affects load balancing and communication overhead directly. It's difficult not only because the graph is vast, but also because we can't assume that the data can be structured in any way to maximize the partitioning algorithm's speed. Instead, the algorithm must follow the system's and other applications' data and programming models. We present a multi-level label propagation (MLP) method for graph partitioning in this paper.

Ground Target Recognition Using Carrier-Free UWB Radar Sensor With a Semi-Supervised Stacked Convolutional Denoising Autoencoder

This paper proposes a scheme for ground-target recognition based on the carrier-free ultra-wideband (UWB) radar sensor for the first time. Carrier-free UWB system emits an extremely short pulse, which can provide potential advantages over other UWB systems, such as high range resolution, short blind range, enhanced anti-multipath jamming ability. These characteristics above guarantee that UWB echoes contain more comprehensive and detailed information with respect to the target of interest. Feature extraction is fundamental and crucial for target recognition. In this paper a deep network named semi-supervised stacked convolutional denoising autoencoder (SCDAE) is developed to extract discriminative features. As an extension of stacked denoising autoencoders (SDAE), SCDAE replaces fully-connected layers with one-dimensional convolutional layers as middle structures. In order to capture essential signatures exactly and improve classification accuracy, we build up a semi-supervised learning mechanism via binding a label regularization term with SCDAE. Moreover, given that echoes observed at different angles belonging to the same target are different, a new multi-level label coding method is proposed and embedded in SCDAE. Experimental results demonstrate that the proposed algorithm can effectively learn essential representation and improve classification accuracy in the presence of low signal-to-noise ratios (SNRs), making it very suitable for use in a classification scheme.

Deep Learning with Taxonomic Loss for Plant Identification.

Plant identification is a fine-grained classification task which aims to identify the family, genus, and species according to plant appearance features. Inspired by the hierarchical structure of taxonomic tree, the taxonomic loss was proposed, which could encode the hierarchical relationships among multilevel labels into the deep learning objective function by simple group and sum operation. By training various neural networks on PlantCLEF 2015 and PlantCLEF 2017 datasets, the experimental results demonstrated that the proposed loss function was easy to implement and outperformed the most commonly adopted cross-entropy loss. Eight neural networks were trained, respectively, by two different loss functions on PlantCLEF 2015 dataset, and the models trained by taxonomic loss led to significant performance improvements. On PlantCLEF 2017 dataset with 10,000 species, the SENet-154 model trained by taxonomic loss achieved the accuracies of 84.07%, 79.97%, and 73.61% at family, genus and species levels, which improved those of model trained by cross-entropy loss by 2.23%, 1.34%, and 1.08%, respectively. The taxonomic loss could further facilitate the fine-grained classification task with hierarchical labels.

Special issue on Big Data Security and Intelligent Data in Clouds (BDS‐IDC)

Special issue on Big Data Security and Intelligent Data in Clouds (<scp>BDS‐IDC</scp>)

Multi-resolution community detection in massive networks.

Aiming at improving the efficiency and accuracy of community detection in complex networks, we proposed a new algorithm, which is based on the idea that communities could be detected from subnetworks by comparing the internal and external cohesion of each subnetwork. In our method, similar nodes are firstly gathered into meta-communities, which are then decided to be retained or merged through a multilevel label propagation process, until all of them meet our community criterion. Our algorithm requires neither any priori information of communities nor optimization of any objective function. Experimental results on both synthetic and real-world networks show that, our algorithm performs quite well and runs extremely fast, compared with several other popular algorithms. By tuning a resolution parameter, we can also observe communities at different scales, so this could reveal the hierarchical structure of the network. To further explore the effectiveness of our method, we applied it to the E-Coli transcriptional regulatory network, and found that all the identified modules have strong structural and functional coherence.

Multi-level labelling: too complex for consumers?

For many consumers, sustainability attributes are a relevant consideration concerning the purchase decision. However, as more food labels enter the retail market it is becoming increasingly difficult for consumers to evaluate the underlying meaning of these labels. The most common labels communicating sustainable values tend to be binary. However, many attributes, such as animal welfare, are of a continuous nature. One solution to communicate differentiated information about process or product standards is by using multi-level labels, which indicate various levels of standards. However, while multi-level labels may help consumers realize the differences in production or process qualities, they may also cause confusion since they convey more complex information. Therefore, the impact of multi-level labels on consumers’ comprehension remains unclear. The primary objective of this paper is to test whether consumers can easily comprehend a multi-level labelling approach by using the case of animal welfare standards. This paper will also assess whether a multi-level label could be an effective tool to communicate information about standards and enhance consumers’ willingness to pay. The results show that when an explanation about the levels of the label is provided, there is an increasing willingness to pay for products with higher standards. Hence, a multi-level label can work if information is provided and is thus an adequate tool to communicate sustainable and ethical values.

Developing food labelling strategies: Multi-level labelling

Labelling is an important cue for consumers as it helps to quickly communicate information about a product or production process. However, the majority of labels on the market are binary, such as labels that indicate whether a product was produced using animal welfare friendly standards or not or whether it was produced organically or not. Yet, there are many intermediate qualities that binary labels do not display. In turn, this could lead to a market failure. A multi-level label can show different process standards of products explicitly. Nonetheless, before launching a multi-level labelling system, it should be tested if a multi-level labelling system can shift market shares in favour of the labelled products. Using a consumer study with 1538 German consumers and the example of animal welfare the shares of product choices are calculated. Two comparisons of the shares of product choices will be made, one between no label and a binary label and the other between no label and a multi-level label. The results suggest that a multi-level labelling system achieves higher market shares, therefore boosts sustainability attributes and can result in higher revenues or sales. The results deliver important information for policymakers and industry.

Internet-based interactive visualization method of 3D lunar model with texture

Because of the huge size of the laser altimetry dataset and the CCD dataset obtained by CE-1 (Chang'E-1), how to use these data to visualize a lunar model with texture in real time through the Internet is a problem. This paper proposes a visualization method for a 3D lunar model with texture. The method increases the speed of data transmission through the Internet by using texture divide-and-conquer techniques, view-dependent evaluation of texture piece visibility, and progressive transmission and reconstruction techniques. Using this method, the convergence problem in the polar regions is solved, and multilevel labels can be added and displayed. An Internet-based client/server architecture has been built and implemented, which can be interactively browsed on a client almost in real time. This work offers a popular application (i.e., using personal terminals and the Internet), and the proposed modeling and visualization method could be applied to most global space-exploration datasets.

A Information-Flow-Based Verification Solution with Security Sensitivity to Check Security Policy of SELinux

According to the implementation of multi-policy in SELinux,the authors introduce the multi-level sensitivity label into the traditional information flow analysis.On the basis of automata and linear temporal logic,an improved information flow analysis is proposed.It can verify both the integrity and confidentiality of the SELinux policies.