Labeling Problem Research Articles

A machine learning approach to extracting spatial information from geological texts in Chinese

Texts have become an important spatial data resource. Interpretation of unstructured geoscience texts using natural language processing methods can effectively facilitate the discovery and retrieval of geographic information. Yet studies on the extraction of spatial information from textual geoscience data are limited compared to digital geoscience data. In this work, a machine learning approach is proposed for mining spatial relations in Chinese geological texts. The approach views spatial relation extraction as a sequence labeling problem, avoids the division of relation categories, and enables mining fine-grained spatial relations. The extracted geological texts commonly describe three-dimensional spatial relations among regions, strata, and lithologies. The extracted spatial relations are classified into three major categories (topological relations, absolute directional relations and relative directional relations) and 14 subcategories. We validated the proposed model with a test dataset, constructed visual displays of the extracted spatial relations on different topics, and quantified the uncertainty in the process from spatial entity recognition to spatial relation extraction. With the detailed portrayal of these spatial relations, this study provides support for solving theoretical and practical problems of cognition, prediction, decision-making, and evaluation in geoscience.

Automatic Prediction of Semantic Labels for French Medical Terms.

We address the problem of semantic labeling of terms in two French medical corpora with the subset of the UMLS. We perform two experiments relying on the structure of words and terms, and on their context: 1) the semantic label of already identified terms is predicted; 2) the terms are detected in raw texts and their semantic label is predicted. Our results show over 0.90 F-measure.

Generative knowledge transfer for ship detection in SAR images

This paper proposes a generative transfer learning framework consisting of a knowledge transfer network and a ship detection network to address the data acquisition and labeling problem for ship detection in Synthetic Aperture Radar (SAR) images. The knowledge transfer network generates pseudo-SAR images whose spatial distributions are consistent with labeled optical images and feature distributions are similar to SAR images. These pseudo-SAR images are further used to improve the generalization performance of convolutional neural network-based detection models. Experimental results on SAR Ship Detection Dataset (SSDD) and AIR-SARShip-1.0 Dataset confirm that the pseudo-SAR images generated by our method can benefit the final detection prediction even no labeled SAR image is given at the training stage. The proposed framework can also significantly reduce the probability of missing alarms and false positives in complex backgrounds.

On the Deduction of theEPPfrom Labeling*

AbstractThe present paper examines a recent attempt to deduce the EPP from labeling. Chomsky (2015) argues that the EPP is reducible to Label Weakness. In this paper, I first point out that Label Weakness raises problems that are not faced under the EPP. I then propose that pair‐Merge (i.e. the Merge that yields an asymmetric structure) provides a solution to Label Weakness, claiming that Merge, together with Label Weakness, explains the EPP. I show that the proposal brings theoretically and empirically favorable consequences. Given the proposed analysis, I also argue that externalization is the other side of the EPP, demonstrating that it explains the EPP when a labeling problem is solved and that it reduces parametric variations with the EPP to the peripheries of the computation. This paper argues that Merge plays a key role in language and endorses the Merge‐based language design proposed in the Minimalist Program.

User preference mining based on fine-grained sentiment analysis

User preference mining is an application of data mining that attracts increasing attention. Although most of the existing user preference mining methods achieved significant performance improvement, the sentiment tendencies of users were seldom considered. This paper proposes fine-grained sentiment analysis for preference mining. The powerful feature representation capabilities of deep neural networks have significantly improved the performance of fine-grained sentiment analysis. But two main challenges remain when using deep neural network models: incomplete user feature extraction and insufficient interaction. In response, a pre-training language model is employed to encode user features to fully explore potential interests of users, a linguistic knowledge model is introduced to assist the encoding, a multi-scale convolution neural network is adopted to capture text features at different scales and fully utilize the text information, and the fine-grained sentiment analysis task is modeled as a sequence labeling problem to explore the sentiment polarity of user evaluation. Experiments on a user review data set are used to verify the new approach. Experimental results of precision, recall rate and F1-value show that the proposed approach performs better, and is more effective than baseline models. For example, the F1-value is increased by 4.27% compared to the best performing baseline model. Findings have important implications for research and practice.

Multi-label learning with missing features and labels and its application to text categorization

• This paper proposes a new method MMFL to address the problem of missing features and missing labels simultaneously in multi-label learning. • Matrix decomposition technology is used to recover the missing values of features and labels. In addition, to overcome the problem of tail labels in matrix factorization, an extra classifier for the sparse tail labels is built. • The experiment on nine multi-label text categorization data sets show that our proposed method achieves a competitive performance in multi-label learning with missing features and labels. In multi-label learning, researchers usually assume that the training data set is complete. However, this assumption is not always hold in real applications, e.g., the features or labels are missing for some data examples. Existing algorithms mainly focus on the problem of missing labels, and ignore the problem of missing features. This paper proposes a novel multi-label learning algorithm named MMFL, i.e., Multi-label learning with Missing Features and Labels, which can deal with the problem of missing features and labels simultaneously. First, we try to recover the missing values of features and labels by matrix factorization, and then learn a classification model from the latent feature space to the latent label space. Second, to overcome the problem of tail labels in matrix factorization, we build an extra classifier for the sparse tail labels. Besides, the manifold regularization technology is used to keep the manifold structures of instance similarity and label correlation. The effectiveness of our proposed method is verified by comparing it with the state-of-the-art approaches over eight multi-label benchmark data sets.

Personalizing label prediction for GitHub issues

Automated label prediction tools can help developers manage and categorize issues on GitHub. However, different open-source projects use various forms of labels with the same meaning. Previous label prediction methods mainly solve the problem of the synonymous labels by manual preprocessing rules, but these preprocessing rules can only identify synonyms with the same prefix or suffix. These factors inspire us to propose a method to identify these synonymous labels automatically and recommend personalized labels for different open-source projects. In this paper, we propose a Personalizing Label Prediction framework for Issues named PLPI. PLPI identifies labels with similar meanings by representing labels as semantic vectors and applying clustering methods . PLPI can predict personalized labels from the existing labels in the open-source project. We conduct a comprehensive study to compare seven commonly adopted labeling models with our approach. The experimental results demonstrate the advantages of our approach. Finally, we show some representative examples and discuss the visualization results of synonyms clustering by dimension reduction. The experimental results show that our method PLPI can improve label prediction performance and provide personalized label recommendation results for different open-source projects.

Learning interactive multi‐object segmentation through appearance embedding and spatial attention

Deep learning approaches to interactive image segmentation are typically formulated as a binary labeling problem. A model trained to make predictions within a fixed set of labels (i.e., foreground and background labels) cannot be used to directly predict the binary masks of multiple objects of interest, which greatly limits its flexibility and adaptivity. The use of different classes of clicks as input is opted for and the first end-to-end learning model for multi-object segmentation, based on a new designed neural network, is developed. The network consists of a visual feature extractor, a recurrent attention module and a dynamic segmentation head, extracts user click-adapted appearance embedding features and spatial attention features, and then learns to transform this information into a segmentation of multiple objects. It is also proposed to train the network using a joint loss function, taking the embedding learning into account for segmentation. Comprehensive experiments are conducted on three benchmark datasets to demonstrate the effectiveness of the proposed method. It performs favorably against state-of-the-art approaches on the multiple object segmentation task, for example, with 0.15 s per image, 0.06 s per object and mean IoU & F1 score of 84.90% on Pascal VOC 2012 validation set. It is further shown that the method can be used in numerous vision applications such as image recoloring and colorization.

The SU(3) ⊃ SO(3) missing label problem and the analytical Bethe Ansatz

The missing label for basis vectors of [Formula: see text] representations corresponding to the reduction [Formula: see text] can be provided by the eigenvalues of [Formula: see text] scalars in the enveloping algebra of [Formula: see text]. There are only two such independent elements of degrees three and four. It is shown how the one of degree four can be diagonalized using the analytical Bethe ansatz.

Sensor Combination Selection Strategy for Kayak Cycle Phase Segmentation Based on Body Sensor Networks

Motion capture technology has been widely used in the sport analysis to improve their performance and reduce the injury risk. Kayak, a popular outdoor sport, employs the coordination of multiple muscles and skeletons, especially those of upper limbs that must be investigated carefully. The fine-time phase segmentation of rowing cycle plays an important role in analyzing kayaker&#x2019;s technique. Aiming at the problem of laborious manual phase labeling in the traditional video analysis method, an automatic phase segmentation method for kayak rowing is proposed combined with a machine learning algorithm. In this article, inertial sensors and a data fusion algorithm are used to calculate the joint angles between arm and trunk, left elbow and right elbow when the athlete is rowing. According to the permutation and combination principle, the angle sequence is combined in nine different ways, and four machine learning algorithms (decision tree, support vector machine, <inline-formula> <tex-math notation="LaTeX">$k$ </tex-math></inline-formula>-nearest neighbor, bagging ensemble learning) are used to study the effects of different combinations on rowing phase division. Among them, the precision of phase segmentation becomes higher with the increase of motion information. The combination of arm to trunk joint angle only needs three data collection nodes; thus, the computational cost is smaller; moreover, all the four algorithms show good classification accuracy (up to 98.1&#x0025;). The results indicating that the combination of arm to trunk joint angle and support vector machine algorithm could better complete the task of the phase segmentation for kayak rowing.

Improved cross entropy loss for noisy labels in vision leaf disease classification

The predictive performance of supervised learning algorithms depends on the quality of labels. In a typical label collection process, multiple annotators provide subjective noisy estimates of the “truth” under the influence of their varying skill-levels and biases. Blindly treating these noisy labels as the ground truth limits the accuracy of learning algorithms in the presence of strong disagreement. This problem is critical for applications in domains where the annotation cost is high. Such problems are extremely serious in the domain of agricultural imaging in leaf disease classification. Due to the limitation of acquisition methods, noisy examples are often not only the normal mislabelled ones, but also the samples which contain more than one instance. To cope with the combination of the two noises, label smoothing is blended in the point of increasing entropy for uncertain labels, with Taylor cross entropy loss, which is proved to be efficient to solve the problem of artificial noisy labels on public datasets. And the proposed method is called smooth-Taylor cross entropy loss, which can deal with the real-world noises in vision leaf disease dataset. Extensive experimental results on cassava leaf disease dataset demonstrate that our proposed approach significantly outperforms the state-of-the-art counterparts.

Deep Neural Networks Algorithm for Vietnamese Word Segmentation

Traditional Vietnamese word segmentation methods do not perform well in the face of Vietnamese ambiguity, in response to the enormous challenge posed by the scarcity of the Vietnamese corpus to language processing. We first investigated the most advanced deep neural network method. According to the ambiguity problem of Vietnamese word segmentation, we then proposed a Vietnamese word segmentation processing technology based on an improved long short-term memory neural network (LSTM), which is made up of an LSTM encoding and a CNN feature extraction portion. The previous important information is kept in the memory unit; the word segmentation processing task is refined into a classification problem and a sequence labeling problem, which can gain the useful features of the word segmentation character and word level automatically. The limitation of the local context window size is avoided, and the word segmentation processing task is refined into a classification problem and a sequence labeling problem. Finally, validated by a homemade Vietnamese news website crawler dataset, the experimental results show that, compared with the single LSTM, single CNN methods, and traditional methods, the performance improvement of our proposed method is more obvious. In the Vietnamese word separation task, the accuracy reaches 96.6%, the recall reaches 95.2%, and the F1 value reaches 96.3%, which is significantly better than the traditional methods CNN and LSTM.

Pengembangan Plastik Pengemas Obat yang Informatif dan Beradsorben

The problem of labeling on plastic medicine packaging which is less informative and its function in maintaining drug quality is very crucial. The purpose of this research and development is to produce informative and absorbent drug packaging plastics. The method used is the ADDIE research and development model. The development stage is carried out by testing physical stability and chemical stability testing. The implementation phase was carried out by asking for responses from 10 pharmacists and 15 ordinary people. Based on the results of the physical stability test, the drug stored in the plastic packaging of the drug that was developed was more physically stable. Based on the results of chemical stability tests, the absorbance of drugs stored in drug packaging plastics developed is more stable than ordinary drug packaging plastics, which is indicated by a smaller slope value. Based on the questionnaires distributed to pharmacists, they strongly agree with the adequacy of the information and agree with the readability, attractiveness of appearance, and usefulness of the adsorbent in the plastic drug packaging developed. Based on the questionnaires distributed to the general public, they strongly agree with the adequacy of the information and agree with the readability, attractiveness of appearance, and the usefulness of the adsorbent in the plastic drug packaging developed.

Application of the Combinatorial Nullstellensatz to Integer-magic Graph Labelings

Let $A$ be a nontrivial abelian group and $A^* = A \setminus \{0\}$. A graph is $A$-magic if there exists an edge labeling $f$ using elements of $A^*$ which induces a constant vertex labeling of the graph. Such a labeling $f$ is called an $A$-magic labeling and the constant value of the induced vertex labeling is called an $A$-magic value. In this paper, we use the Combinatorial Nullstellensatz to show the existence of $\mathbb{Z}_p$-magic labelings (prime $p \geq 3$ ) for various graphs, without having to construct the $\mathbb{Z}_p$-magic labelings. Through many examples, we illustrate the usefulness (and limitations) in applying the Combinatorial Nullstellensatz to the integer-magic labeling problem. Finally, we focus on $\mathbb{Z}_3$-magic labelings and give some results for various classes of graphs.

The intelligent decision-making of copper flotation backbone process based on CK-XGBoost

This study creatively puts forward XGBoosts with correlation-based and knowledge-based function (CK-XGBoost) to design flotation backbone process according to the natural properties of a copper mine. The decision-making of flotation backbone process of copper mine is a multi label problem with high feature dimension and small instance set. The proposed CK-XGBoost selects label-specific features and trains a binary XGBoost for each label, and the information of other labels is used to assist the classifier modeling through correlation-based function and knowledge-based function. The correlation-based function utilizes the Pearson correlation coefficient between labels in training set, so that the predicted values of two strong positive correlation labels are similar and the predicted values of two strong negative correlation labels are contrary. The knowledge-based function utilizes domain knowledge, which is independent of the distribution of training set. The experimental results demonstrate the significantly superiority of the proposed CK-XGBoost in the decision-making of copper flotation backbone process.

Handling partially labeled network data: A semi-supervised approach using stacked sparse autoencoder

Network traffic analytics has become a crucial task in order to better understand and manage network resources, especially in the network softwarization era where the implementation of this concept can be done easily with network function virtualization. Currently, many approaches have been proposed to improve the performance of traffic classification. However, as new types of traffic emerge every day (and they are generally not labeled), this opens a new challenge to be handled. Moreover, the question of how to accurately classify the traffic using a limited amount of labeled data or partially labeled data is also another important concern. In fact, labeling data is often difficult and time-consuming. In order to solve the previously described issues, we reformulate traffic classification into a semi-supervised learning where both supervised learning (using labeled data) and unsupervised learning (no label data) are combined. To do so, this paper presents a stacked sparse autoencoder (SSAE) based semi-supervised deep-learning model for traffic classification. The main motivations of this approach are: (i) unlabeled data is often abundant and easily available; (ii) classification performance of the whole model can be greatly improved when a large amount of unlabeled traffic is included in the training process; (iii) there is a limit to how much human effort can be thrown at the labeling problem. To investigate the performance of our approach, an empirical study has been conducted on a real dataset and results indicate that using a large amount of unlabeled data in the SSAE pre-trained phase can improve significantly the classification performance of the whole model. Furthermore, the proposed approach is compared against other representative machine-learning and deep-learning models, which are Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), Multi-Layer Perceptron (MLP), eXtreme Gradient Boosting (XGBoost), and Autoencoder.

Towards improving the robustness of sequential labeling models against typographical adversarial examples using triplet loss

AbstractMany fundamental tasks in natural language processing (NLP) such as part-of-speech tagging, text chunking, and named-entity recognition can be formulated as sequence labeling problems. Although neural sequence labeling models have shown excellent results on standard test sets, they are very brittle when presented with misspelled texts. In this paper, we introduce an adversarial training framework that enhances the robustness against typographical adversarial examples. We evaluate the robustness of sequence labeling models with an adversarial evaluation scheme that includes typographical adversarial examples. We generate two types of adversarial examples without access (black-box) or with full access (white-box) to the target model’s parameters. We conducted a series of extensive experiments on three languages (English, Thai, and German) across three sequence labeling tasks. Experiments show that the proposed adversarial training framework provides better resistance against adversarial examples on all tasks. We found that we can further improve the model’s robustness on the chunking task by including a triplet loss constraint.

The bounds for the distance two labelling and radio labelling of nanostar tree dendrimer

The distance two labelling and radio labelling problems are applicable to find the optimal frequency assignments on AM and FM radio stations. The distance two labelling, known as L(2,1)-labelling of a graph A, can be defined as a function, 𝑘, from the vertex set V(A) to the set of all nonnegative integers such that 𝑑(𝑐, 𝑠) represents the distance between the vertices c and s in 𝐴 where the absolute values of the difference between 𝑘(𝑐) and 𝑘(𝑠) are greater than or equal to both 2 and 1 if 𝑑(𝑐, 𝑠)=1 and 𝑑(𝑐, 𝑠) = 2, respectively. The L(2,1)-labelling number of 𝐴, denoted by 𝜆2,1 (𝐴), can be defined as the smallest number j such that there is an 𝐿(2,1) −labeling with maximum label j. A radio labelling of a connected graph A is an injection k from the vertices of 𝐴 to 𝑁 such that 𝑑(𝑐, 𝑠) + |𝑘(𝑐) − 𝑘(𝑠)| ≥ 1 + 𝑑 ∀ 𝑐, 𝑠 ∈ 𝑉(𝐴), where 𝑑 represents the diameter of graph 𝐴. The radio numbers of 𝑘 and A are represented by 𝑟𝑛(𝑘) and 𝑟𝑛(𝐴) which are the maximum number assigned to any vertex of 𝐴 and the minimum value of 𝑟𝑛(𝑘) taken over all labellings k of 𝐴, respectively. Our main goal is to obtain the bounds for the distance two labelling and radio labelling of nanostar tree dendrimers.

Unified Graph-Based Missing Label Propagation Method for Multilabel Text Classification

In multilabel classification, each sample can be allocated to multiple class labels at the same time. However, one of the prominent problems of multilabel classification is missing labels (incomplete labels) in multilabel text. The multilabel classification performance is reduced significantly with the presence of missing labels. In order to address the incomplete or missing label problem, this study proposes two methods: an aggregated feature and label graph-based missing label handling method (GB-AS), and a unified graph-based missing label propagation method (UG-MLP). GB-AS is used to obtain an initial label matrix based on the similarity of both document levels: feature-based weighting representation and label-based weighting representation. On the other hand, UG-MLP is introduced to construct a mixed graph that combines GB-AS and label correlations into a single groundwork. A high-order label correlation is learned from the incomplete training data and applied to supplement the missing label matrix, which guides the creation of multilabel classification models. The combination of the mixed graphs by UG-MLP is aimed to obtain the benefits of both graphs to increase the classification performance. To evaluate UG-MLP, the metrics of precision, recall and F-measure were used on three benchmark datasets, namely, the Reuters-21578, Bibtex and Enron datasets. The experimental results show that UG-MLP outperformed GB-AS as well as other state-of-the-art approaches. Therefore, we can infer from the findings that by plotting a unified graph based on joining aggregated feature and label weightings together with the label correlation, the performance of multilabel classification can be improved.

An ASP approach for arteries classification in CT scans

Abstract Automated segmentation of computed tomography (CT) scans is the first step in the pipeline for the interpretation and identification of potential pathologies in human organs. Several methods based on machine learning (ML) are currently available, even if their precision is still outperformed by medical doctors. In this field there are some intrinsic limitations to ML approaches, such as the following: cost and time to acquire high-quality annotated scans for training; and a remarkable high variability of organ morphology due to age, conditions, genetics and acquisition. This paper outlines a new methodology based on Answer Set Programming, which returns reliable, easy-to-program and explainable interpretations. In particular, we focus on the CT scan analysis and retrieval of tree-like structure, corresponding to main blood vessels (arteries) arrangement. The structure is compared to the knowledge base of vessels contained in anatomy textbooks. The mapping of vessel names is computed by an Answer Set Programming program. This preliminary step produces a robust input to a reasoner for the multi-organ labelling and localization problem.