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Rumors can cause devastating consequences to individuals and our society. Analysis shows that the widespread of rumors typically results from deliberate promotion of information aiming to shape the collective public opinions on the concerned event. In this paper, we combat such chaotic phenomenon with a countermeasure by mirroring against how such chaos is created to make rumor detection more robust and effective. Our idea is inspired by adversarial learning method originated from Generative Adversarial Networks (GAN). We propose a GAN-style approach, where a generator is designed to produce uncertain or conflicting voices, further polarizing the original conversational threads to boost the discriminator. We reveal that feature learning effectiveness is highly relevant to the quality of generated parody. Given the strong natural language generation performance of transformer, we propose a transformer-based method to improve the generated posts, which appear to be closely responsive to the source post and retain the authentic propagation structure. Different from traditional data-driven rumor detection approaches, our method can capture low-frequency but more salient non-trivial discriminant patterns. Extensive experiments on THREE benchmarks demonstrate that our rumor detection method achieves much better results than state-of-the-art methods, and the transformer-based model further improve the performance of our GAN-style approach.

With the development of science and technology, the rapid development of social economy, the motor as a new type of transmission equipment, in the production and life of people occupies a pivotal position. Under the rapid development of computer and electronic technology, manufacturing equipment is becoming larger, faster, more continuous, and more automated. This has resulted in complex, expensive, accident-damaging, and high-impact equipment for electric motors; even routine maintenance requires significant equipment maintenance and maintenance costs. If a fault occurs, it will cause serious damage to the entire equipment and can even have a major impact on the entire production process, leading to a serious economic and social life. In this paper, a CNN-based machine learning fault diagnosis method is proposed to address the problem of high incidence of motor faults and difficulty in identifying fault types. A fault reproduction test is constructed by machine learning techniques to extract vibration time domain data for normal operating conditions, rotor eccentricity, stator short circuit, and bearing inner ring fault; divide the data segment into 15 speed segments, extract 13 typical time domain features for each speed segment; and perform mathematical statistics for fault diagnosis. Compared with the traditional algorithm, the method has more comprehensive feature information extraction, higher diagnostic accuracy, and faster diagnostic speed, with a fault diagnosis accuracy of 98.7%.

International audience; Advanced technologies for the calibration of machine tool are presented. Geometric, kinematic, thermally-induced and load-induced errors are classified into errors within one-axis as intra-axis errors, errors between axes as inter-axis errors, and as volumetric errors. As the major technological elements of machine tool calibration, the measurement methods, mathematical models and compensation approaches of the machine tool errors are addressed. The criteria for selecting a combination of the technological elements for machine tool calibration from the point of view of accuracy, complexity, and cost are provided. Recent applications of artificial intelligence and machine learning in machine tool calibration are introduced. Remarks are also made on future trends in machine tool calibration.

With the continuous integration of deep learning and the technique of molecular biology, target detection models must accurately detect the position of each cell in the image and classify it correctly. We present a model for the multi-scale feature fusion of the existing human cell image dataset based on Gaussian mixedly clustering. First, a novel feature extraction network for extracting preliminary features at picture multi scales was presented, which was based on a residual neural network with Instance Normalization and a Mish activation function. Second, the presented model adopts the idea of feature fusion and introduced a new type of feature fusion network to integrate feature graphs on different scales. Furthermore, a Gaussian hybrid clustering algorithm was proposed to cluster the hyperparameters. Based on the experimental results, the average accuracy of the proposed model in the human cell image dataset exceeds 0.96, which improves by 11.9% compared with the existing target detection methods in the same field. Experiments show that the proposed model had been adapted to datasets with uneven sample distribution, providing new ideas for research on medical images.

Applying machine learning to hyperspectral remote sensing is a new way to solve practical problems. A new method that combines spectral indexes and Naive Bayes, named SINB, was proposed to distingu...

Emergency response activity relies on transportation networks. Emergency facility location interacts with transportation networks clearly. This review is aimed to provide a combined framework for emergency facility location in transportation networks. The article reveals emergency response activities research clusters, issues, and objectives according to keywords co-occurrence analysis. Four classes of spatial separation models in transportation networks, including distance, routing, accessibility, and travel time are introduced. The stochastic and time-dependent characteristics of travel time are described. Travel time estimation and prediction method, travel time under emergency vehicle preemption, transportation network equilibrium method, and travel time in degradable networks are demonstrated. The emergency facilities location models interact with transportation networks, involving location-routing model, location models embedded with accessibility, location models embedded with travel time, and location models employing mathematical program with equilibrium constraints are reviewed. We then point out the-state-of-art challenges: ilities-oriented, evolution landscape and sequential decision modelling, data-driven optimization approach, and machine learning-based algorithms.

A brain-computer interface (BCI) measures and analyzes brain activity and converts this activity into computer commands to control external devices. In contrast to traditional BCIs that require a subject-specific calibration process before being operated, a subject-independent BCI learns a subject-independent model and eliminates subject-specific calibration for new users. However, building subject-independent BCIs remains difficult because electroencephalography (EEG) is highly noisy and varies by subject. In this study, we propose an invariant pattern learning method based on a convolutional neural network (CNN) and big EEG data for subject-independent P300 BCIs. The CNN was trained using EEG data from a large number of subjects, allowing it to extract subject-independent features and make predictions for new users. We collected EEG data from 200 subjects in a P300-based spelling task using two different types of amplifiers. The offline analysis showed that almost all subjects obtained significant cross-subject and cross-amplifier effects, with an average accuracy of more than 80%. Furthermore, more than half of the subjects achieved accuracies above 85%. These results indicated that our method was effective for building a subject-independent P300 BCI, with which more than 50% of users could achieve high accuracies without subject-specific calibration.

Review rating prediction is commonly approached from the perspective of either Collaborative Filtering (CF) or Sentiment Classification (SC). CF-based approach usually resorts to matrix factorization based on user–item interaction, and does not fully utilize the valuable review text features. In contrast, SC-based approach is focused on mining review content, but can just incorporate some user- and product-level features, and fails to capture sufficient interactions between them represented typically in a sparse matrix as CF can do. In this paper, we propose a novel, extensible review rating prediction model called InterSentiment by bridging the user-product interaction model and the sentiment model based on deep learning. InterSentiment is a specific instance of our proposed Deep Learning based Collaborative Filtering framework. The proposed model aims to learn the high-level representations combining user-product interaction and review sentiment, and jointly project them into the rating scores. Results of experiments conducted on IMDB and two Yelp datasets demonstrate clear advantage of our proposed approach over strong baseline methods.

Rumor spread in social media severely jeopardizes the credibility of online content. Thus, automatic debunking of rumors is of great importance to keep social media a healthy environment. While facing a dubious claim, people often dispute its truthfulness sporadically in their posts containing various cues, which can form useful evidence with long-distance dependencies. In this work, we propose to learn discriminative features from microblog posts by following their non-sequential propagation structure and generate more powerful representations for identifying rumors. For modeling non-sequential structure, we first represent the diffusion of microblog posts with propagation trees, which provide valuable clues on how a claim in the original post is transmitted and developed over time. We then present a bottom-up and a top-down tree-structured models based on Recursive Neural Networks (RvNN) for rumor representation learning and classification, which naturally conform to the message propagation process in microblogs. To enhance the rumor representation learning, we reveal that effective rumor detection is highly related to finding evidential posts, e.g., the posts expressing specific attitude towards the veracity of a claim, as an extension of the previous RvNN-based detection models that treat every post equally. For this reason, we design discriminative attention mechanisms for the RvNN-based models to selectively attend on the subset of evidential posts during the bottom-up/top-down recursive composition. Experimental results on four datasets collected from real-world microblog platforms confirm that (1) our RvNN-based models achieve much better rumor detection and classification performance than state-of-the-art approaches; (2) the attention mechanisms for focusing on evidential posts can further improve the performance of our RvNN-based method; and (3) our approach possesses superior capacity on detecting rumors at a very early stage.

Making an erroneous decision may cause serious results in diverse mission-critical tasks such as medical diagnosis and bioinformatics. Previous work focuses on classification with a reject option, i.e., abstain rather than classify an instance of low confidence. Most mission-critical tasks are always accompanied with class imbalance and cost sensitivity, where AUC has been shown a preferable measure than accuracy in classification. In this work, we propose the framework of AUC optimization with a reject option, and the basic idea is to withhold the decision of ranking a pair of positive and negative instances with a lower cost, rather than mis-ranking. We obtain the Bayes optimal solution for ranking, and learn the reject function and score function for ranking, simultaneously. An online algorithm has been developed for AUC optimization with a reject option, by considering the convex relaxation and plug-in rule. We verify, both theoretically and empirically, the effectiveness of the proposed algorithm.

A novel machine learning aided structural reliability analysis for functionally graded frame structures against static loading is proposed. The uncertain system parameters, which include the material properties, dimensions of structural members, applied loads, as well as the degree of gradation of the functionally graded material (FGM), can be incorporated within a unified structural reliability analysis framework. A 3D finite element method (FEM) for static analysis of bar-type engineering structures involving FGM is presented. By extending the traditional support vector regression (SVR) method, a new kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is proposed for modelling the underpinned relationship between the structural behaviours and the uncertain system inputs. The proposed structural reliability analysis inherits the advantages of the traditional sampling method (i.e., Monte-Carlo Simulation) on providing the information regarding the statistical characteristics (i.e., mean, standard deviations, probability density functions and cumulative distribution functions etc.) of any concerned structural outputs, but with significantly reduced computational efforts. Five numerical examples are investigated to illustrate the accuracy, applicability, and computational efficiency of the proposed computational scheme.

In the era of data, major decisions are determined by massive data, especially in the healthcare industry. In this paper, an intelligent data-driven model is proposed based on machine learning theory, specifically, support vector machine (SVM) and random forest (RF). The model is then applied to a case of disease diagnosis, cough variant asthma (CVA). The data of 137 samples with 12 attributes is collected for experiments. The results show that the proposed model achieves better prediction performance than single SVM and single RF. Besides, in order to identify the key medical indicators to enhance diagnosis accuracy and efficiency, the most important factors affecting CVA are generated by the proposed model, including FENO, EOS%, MMEF75/25, FEV1/FVC, PEF, etc. Meanwhile, it is demonstrated that the proposed model could be a user-friendly tool to improve the performance of disease diagnosis.

For visual discomfort prediction (VDP) of stereoscopic 3D images, a common two-stage framework is to first extract features that are predictive of the experienced visual discomfort level when viewing stereoscopic images and then use typical regression tools to learn the mapping from the extracted features to visual discomfort scores. Most existing approaches for stereoscopic 3D VDP focus on the former stage, i.e. , feature extraction, while limited efforts have dedicated to exploiting more powerful and robust learning algorithms in this field. In this letter, inspired by the pairwise comparison-based subjective evaluation methodology, we propose a novel Risk-Aware Pairwise Rank Learning (RAPRL) approach to further improve the prediction accuracy. Unlike the traditional VDP approaches using different regression tools for feature-score mapping, our proposed RARL method addresses this problem based on a completely different pairwise rank learning framework with a risk-aware constraint. Experiments have verified the effectiveness and robustness of our proposed VDP model using RAPRL as the learning algorithm.

Collaborative learning has gained great popularity due to its benefit of data privacy protection: participants can jointly train a Deep Learning model without sharing their training sets. However, recent works discovered that an adversary can fully recover the sensitive training samples from the shared gradients. Such reconstruction attacks pose severe threats to collaborative learning. Hence, effective mitigation solutions are urgently desired. In this paper, we propose to leverage data augmentation to defeat reconstruction attacks: by preprocessing sensitive images with carefully-selected transformation policies, it becomes infeasible for the adversary to extract any useful information from the corresponding gradients. We design a novel search method to automatically discover qualified policies. We adopt two new metrics to quantify the impacts of transformations on data privacy and model usability, which can significantly accelerate the search speed. Comprehensive evaluations demonstrate that the policies discovered by our method can defeat existing reconstruction attacks in collaborative learning, with high efficiency and negligible impact on the model performance., Comment: 17 pages, 16 figures, CVPR2021 oral

Face recognition is greatly improved by deep convolutional neural networks (CNNs). Recently, these face recognition models have been used for identity authentication in security sensitive applications. However, deep CNNs are vulnerable to adversarial patches, which are physically realizable and stealthy, raising new security concerns on the real-world applications of these models. In this paper, we evaluate the robustness of face recognition models using adversarial patches based on transferability, where the attacker has limited accessibility to the target models. First, we extend the existing transfer-based attack techniques to generate transferable adversarial patches. However, we observe that the transferability is sensitive to initialization and degrades when the perturbation magnitude is large, indicating the overfitting to the substitute models. Second, we propose to regularize the adversarial patches on the low dimensional data manifold. The manifold is represented by generative models pre-trained on legitimate human face images. Using face-like features as adversarial perturbations through optimization on the manifold, we show that the gaps between the responses of substitute models and the target models dramatically decrease, exhibiting a better transferability. Extensive digital world experiments are conducted to demonstrate the superiority of the proposed method in the black-box setting. We apply the proposed method in the physical world as well., Comment: Accpeted by CVPR 2021. Based on the camera ready version, some typos are fixed

Background: Tuberculosis is a major global public health problem. However, it is still in the exploratory stage that the study of the meteorological factors related to the incidence of tuberculosis in Shanxi Province. Therefore, it is very urgent to establish an early warning system that easily operate of tuberculosis. Method: The epidemiological characteristics of tuberculosis in Shanxi Province were described, and the Dynamic Bayesian Network early warning model was established by time series cross-correlation analysis and Bayesian Network.Results: 1. The reported incidence of tuberculosis in Shanxi Province showed an overall downward trend from 2008 to 2017, showing a phenomenon of high in the middle and low at both ends each year, with certain seasonal characteristics. 2. Based on the results of cross-correlation analysis, it is reasonable to use dynamic Bayesian model fitting with meteorological factors lagging for 2 months; the monthly average temperature and monthly precipitation are positively correlated with the incidence of tuberculosis, but the monthly mean air pressure is negatively. 3. Comparison of classification and recognition performance of the three models shows that DBN has the highest classification accuracy in the two regions, which indicates that DBN is better than the other two models in reflecting the performance of minority classes, and better for the comprehensive classification of minority classes and majority classes. Conclusion: 1. Shanxi Province has tuberculosis clustering in time, space and time and space. Incidence peak is in spring and early summer. March is the highest month in the year. Seven meteorological factors such as monthly precipitation are the main factors affecting the incidence of tuberculosis in Shanxi Province. 2. The classification and recognition performance of the Dynamic Bayesian Network early warning model of tuberculosis-meteorological factors established in this study is significantly better than that of static Bayesian Network and support vector machine model, and can better predict the future.

In recent years, deep neural networks (DNNs) have achieved great successes in many areas, such as cognitive computation, pattern recognition, and computer vision. Although many hand-crafted deep networks have been proposed in the literature, designing a well-behaved neural network for a specific application requires high-level expertise yet. Hence, the automatic architecture design of DNNs has become a challenging and important problem. In this paper, we propose a new reinforcement learning method, whose action policy is to select neural blocks and construct deep networks. We define the action search space with three types of neural blocks, i.e., dense block, residual block, and inception-like block. Additionally, we have also designed several variants for the residual and inception-like blocks. The optimal network is automatically learned by a Q-learning agent, which is iteratively trained to generate well-performed deep networks. To evaluate the proposed method, we have conducted experiments on three datasets, MNIST, SVHN, and CIFAR-10, for image classification applications. Compared with existing hand-crafted and auto-generated neural networks, our auto-designed neural network delivers promising results. Moreover, the proposed reinforcement learning algorithm for deep networks design only runs on one GPU, demonstrating much higher efficiency than most of the previous deep network search approaches.

Micro-videos, a new form of videos that are constrained in duration, gain significant popularity in recent years. The volume and rate of online micro-videos urgently calls for effective recommendation algorithms to help users find their interested ones. Although some previous works have investigated how to model users’ historical behaviors to predict the click-through rate of micro-videos, they are generally based on positive feedback only but overlook the negative which can help understand user preference at a finer granularity. The positive and negative feedback jointly imply the user’s different sentiments on different aspects, where each aspect is one component of a micro-video such as video_scene and video_subject. To this end, we propose an a spect-level s entiment cap sule network(ASCap) for micro-video click-through rate prediction by aggregating both positive and negative feedback, with an attempt to make the prediction more explainable. More specifically, an aspect-specific gating mechanism is firstly utilized to extract the aspect-level features from the target micro-video and the user’s positive and negative feedback. Then, in the following sentiment capsule network, the aspect-level features of the target micro-video are paired with those of positive and negative feedback respectively to identify their sentiments and form the sentiment capsules. Finally, the prediction layer is employed to calculate the overall click probability based on the sentiment capsules. Experimental results on two real-world micro-video datasets demonstrate that the proposed method significantly outperforms the state-of-the-art methods.

Novel coronavirus disease 2019 (COVID-19) is an emerging, rapidly evolving crisis, and the ability to predict prognosis for individual COVID-19 patient is important for guiding treatment. Laboratory examinations were repeatedly measured during hospitalization for COVID-19 patients, which provide the possibility for the individualized early prediction of prognosis. However, previous studies mainly focused on risk prediction based on laboratory measurements at one time point, ignoring disease progression and changes of biomarkers over time. By using historical regression trees (HTREEs), a novel machine learning method, and joint modeling technique, we modeled the longitudinal trajectories of laboratory biomarkers and made dynamically predictions on individual prognosis for 1997 COVID-19 patients. In the discovery phase, based on 358 COVID-19 patients admitted between 10 January and 18 February 2020 from Tongji Hospital, HTREE model identified a set of important variables including 14 prognostic biomarkers. With the trajectories of those biomarkers through 5-day, 10-day and 15-day, the joint model had a good performance in discriminating the survived and deceased COVID-19 patients (mean AUCs of 88.81, 84.81 and 85.62% for the discovery set). The predictive model was successfully validated in two independent datasets (mean AUCs of 87.61, 87.55 and 87.03% for validation the first dataset including 112 patients, 94.97, 95.78 and 94.63% for the second validation dataset including 1527 patients, respectively). In conclusion, our study identified important biomarkers associated with the prognosis of COVID-19 patients, characterized the time-to-event process and obtained dynamic predictions at the individual level.

© 2020 Elsevier B.V. A machine learning aided stochastic nonlocal damage analysis framework is proposed for quasi-brittle materials. The uncertain system parameters, including the material properties and loading actions, have been incorporated and analysed within a unified safety assessment framework against various working conditions. A three-dimensional integral-type nonlocal damage model through finite element method (FEM) has been adopted. For the purpose of investigating the probabilistic damage analysis problems, a freshly established machine learning approach, namely the capped-extended-support vector regression method (C-X-SVR), is proposed to eliminate the influences of random outliers in the first step, then establish the relationship between the uncertain systemic inputs and structural responses. Such that the training robustness and computational adaptability of the proposed regression model can be reinforced. Moreover, the proposed approach is competent of efficiently predicting the statistical information (i.e., means, standard deviations, probability density functions and cumulative density functions) of structural behaviours under continuous information update of the uncertain working condition from mercurial environment. One real-life experimental validation and two numerical investigations are implemented to further verify the effectiveness and efficiency of the uncertainty quantification framework against probabilistic damage analysis.

Background Novel coronavirus disease (COVID-19) is an emerging, rapidly evolving situation. At present, the prognosis of severe and critically ill patients has become an important focus of attention. We strived to develop a prognostic prediction model for severe and critically ill COVID-19 patients.MethodsTo assess the factors associated with the prognosis of those patients, we retrospectively investigated the clinical, laboratory characteristics of confirmed 112 cases of COVID-19 admitted between 21 January to 6 March 2020 from Huangshi Central Hospital, Huangshi Hospital of Traditional Chinese Medicine, and Daye People’s Hospital. We applied machine learning method (survival random forest) to select predictors for 28-day survival and taken into account the dynamic trajectory of laboratory indicators. Results Fifteen candidate prognostic features, including 11 baseline measures (including platelet count (PLT), urea, creatine kinase (CK), fibrinogen, creatine kinase isoenzyme activity, aspartate aminotransferase (AST), activation of partial thromboplastin time (APTT), albumin, standard deviation of erythrocyte distribution width (RBC-SD), neutrophils (%) and red blood cell count (RBC)) and 4 trajectory clusters (changes during hospitalization in the white blood cell (WBC), PLT large cell ratio (P-LCR), PLT distribution width (PDW) and AST), combined with covariates achieved 100% (95%CI: 99%-100%) AUC and reached 87% (95%CI: 84%-91%) AUC in an external validation set. Conclusions Taking advantage of random forest technique and laboratory dynamic measures, we developed a forest model to predict survival outcome of COVID-19 patients, which achieved 87% AUC in the external validation set. Our online tool will help to facilitate the early recognition of patients with high risk.

Multiple Instance Learning (MIL) is a fundamental method for weakly supervised object detection (WSOD), but experiences difficulty in excluding local optimal solutions and may miss objects or falsely localize object parts. In this paper, we introduce discrepantly collaborative modules into MIL and thereby create discrepant multiple instance learning (D-MIL), pursuing optimal solutions in a simple-yet-effective way. D-MIL adopts multiple MIL learners to pursue discrepant yet complementary solutions indicating object parts, which are fused with a collaboration module for precise object localization. D-MIL implements a new “teachers-students” model, where MIL learners act as “teachers” and object detectors as “students”. Multiple teachers provide rich yet complementary information, which are absorbed by students and transferred back to reinforce the performance of teachers. Experiments show that D-MIL significantly improves the baseline while achieves state-of-the-art performance on the challenging MS-COCO object detection benchmark.

A machine learning aided non-deterministic damage prediction framework against both 2D and 3D fracture problems is presented in this paper. By introducing a newly developed extended support vector regression (X-SVR) with generalized Dirichlet feature mapping into the phase field crack growth model, a damage assessment method that contains both crack diagnosis and prognosis is designed. Within the proposed analysis framework, the intricate fracture mechanism of practical engineering system can be learnt by the X-SVR model so a continuous damage diagnosis-prognosis loop can be established to assess the latest working condition of the structure. The proposed framework is applicable not only for quantifying and then assessing the current working conditions, but also for predicting the potentially crack propagation against the future forecasted information. Compared with the established experimental records and numerical result, the accuracy, effectiveness, and computational efficiency of the proposed framework are fully verified.

We describe how to use several machine learning techniques organized in a learning pipeline to segment and identify subcellular structures from cryo electron tomograms. These tomograms are difficult to analyze with traditional segmentation tools. The learning pipeline in our approach starts from supervised learning via a special convolutional neural network trained with simulated data. It continues with semi-supervised reinforcement learning and/or a region merging techniques that try to piece together disconnected components that should belong to the same subcellular structure. A parametric or non-parametric fitting procedure is then used to enhance the segmentation results and quantify uncertainties in the fitting. Domain knowledge is used in generating the training data for the neural network and in guiding the fitting procedure through the use of appropriately chosen priors and constraints. We demonstrate that the approach proposed here work well for extracting membrane surfaces of protein reconstituted liposomes in a cellular environment that contains other artifacts.

In the process of treating pro-diseases with acupuncture, traditional Chinese medicine (TCM) doctors may fine-tune acupuncture prescriptions according to different prior experiences. Different prescriptions will affect the efficiency and effect of acupuncture treatment, and even excessive acupoint selection may cause psychological pressure on patients. We still lack an effective means to analyze the meridian system and acupoint specificity to clarify the mapping relationship between acupoints and diseases. Given the inability of modern medical technology to provide effective evidence support for meridians and acupoints, we combined acupuncture theory with network science for an interdisciplinary discussion. In this paper, we constructed a weighted undirected acupoint-disease network (ADN) based on clinical acupuncture prescription literature and proposed a high-specificity key node mining method based on ADN. Combined with the principle of acupoint selection in TCM, the proposed method balanced the contribution of local areas to the network based on the distribution characteristics of meridians and selected 30 key acupoints with high influence on the global topology according to the evaluation index of key nodes. Finally, we compared the proposed method with the other six classical node importance evaluation algorithms in terms of resolution, network loss, and accuracy. The comprehensive results show that the marked key acupoint nodes make outstanding contributions to the connectivity, topological structure, and weighted benefits of the network, and the stability and specificity of the algorithm guarantee the reliability of the key acupoint nodes. We consider that these key acupoints with high centrality in ADN can be used as core acupoints to help researchers explore targeted and high-impact acupoint combinations under resource constraints and optimize existing acupuncture prescriptions.

With the extensive application of ontology in the fields of information retrieval and artificial intelligence, the ontology-based conceptual similarity calculation becomes a hot topic in ontology research. The essence of ontology learning is to obtain the ontology function through the learning of ontology samples, so as to map the vertices in each ontology graph into real numbers, and finally determine the similarity between corresponding concepts by the difference between real numbers. The essence of ontology mapping is to calculate concepts from different ontologies. In this paper, we introduce new ontology similarity computing in view of stochastic primal dual coordinate method, and two experiments show the effectiveness of our proposed ontology algorithm.

In complex urban environments, the information of high-resolution Aerosol Optical Depth (AOD) is of great importance for effective air pollution control, air navigation, public health assessment, and meteorological forecasting. High resolution AOD may be produced by merging and/or fusing existing AOD products or reproduced by merging and/or fusing the reflectance data at the top of atmosphere (TOA) and ground levels through the deep blue method. However, the former can only lead to the production of AOD with 500 m~1 km spatial resolution at best. To overcome this barrier, it is necessary to fuse the reflectance values of Landsat and MODIS imageries at the TOA level to be in concert with the fused land surface reflectance values for advanced synthesis. Such a collective endeavor can lead to the production of AOD with daily 30m spatial resolution via the deep blue method. This paper thus presents such a synthetic effort that synergizes the spatial and temporal advantages of two satellite sensors (MODIS Terra and Landsat 8) to reach the goal with the aid of machine learning and high-performance computing. Based on the deep blue method, the practical implementation of the synthetic image processing was assessed by a case study of the downtown Atlanta area in the United States. 10-fold cross validation was applied stepwise to control the uncertainty via machine learning. The predictions of AOD at the ground level were calibrated using the AErosol RObotic NETwork (AERONET) AOD data and finally validated by the AERONET) AOD data too.

In clinical medicine, cough is a common disease. Years of cough diagnosis have been collected from a large number of patients as test data. Using these test data, it is possible to find the hidden rules inside these data, which can improve the diagnosis accuracy of cough. In recent years, these related problems have been concerned by the relevant medical staff. From the known medical data, medical data mining and processing can extract knowledge, and summarize the experiences of medical experts. This technology is becoming more and more important in the medical information field. In this paper, cough test attributes ,such as peak expiratory flow and fractional exhaled nitric oxide (FENO), are modeled by decision tree and support vector machine. The experimental results show that FENO and percentage of eosinophils have a great effect on the diagnosis of cough, which are important attributes for cough diagnosis.

In this paper we study multi-label learning with weakly labeled data, i.e., labels of training examples are incomplete, which commonly occurs in real applications, e.g., image classification, document categorization. This setting includes, e.g., (i) semi-supervised multi-label learning where completely labeled examples are partially known; (ii) weak label learning where relevant labels of examples are partially known; (iii) extended weak label learning where relevant and irrelevant labels of examples are partially known. Previous studies often expect that the learning method with the use of weakly labeled data will improve the performance, as more data are employed. This, however, is not always the cases in reality, i.e., weakly labeled data may sometimes degenerate the learning performance. It is desirable to learn safe multi-label prediction that will not hurt performance when weakly labeled data is involved in the learning procedure. In this work we optimize multi-label evaluation metrics ( $$\hbox {F}_1$$ score and Top-k precision) given that the ground-truth label assignment is realized by a convex combination of base multi-label learners. To cope with the infinite number of possible ground-truth label assignments, cutting-plane strategy is adopted to iteratively generate the most helpful label assignments. The whole optimization is cast as a series of simple linear programs in an efficient manner. Extensive experiments on three weakly labeled learning tasks, namely, (i) semi-supervised multi-label learning; (ii) weak label learning and (iii) extended weak label learning, clearly show that our proposal improves the safeness of using weakly labeled data compared with many state-of-the-art methods.

In recent years, as a semantic analysis and computational tool, ontology has been widely applied in many engineering applications. Many cases suggests that it’s confronted with countless big data source with the complex data structures. In order to relieve the dilemma, the sparse learning algorithms are introduced into the ontology similarity measuring and ontology mapping. In this setting, it should be a high dimensional expression of each ontology vertex, and the ontology algorithm should extract key component information effectively. Under such background, we consider the ontology sparse vector learning algorithm and application in different engineering applications. In this article, by means of coordinate descent minimization tricks, we present the ontology sparse vector optimization strategy and discuss the different transformation in different settings. At last, the new ontology sparse vector learning proceeding is applied to four engineering applications respectively to get its efficiency verified.

In biology field, the ontology application relates to a large amount of genetic information and chemical information of molecular structure, which makes knowledge of ontology concepts convey much information. Therefore, in mathematical notation, the dimension of vector which corresponds to the ontology concept is often very large, and thus improves the higher requirements of ontology algorithm. Under this background, we consider the designing of ontology sparse vector algorithm and application in biology. In this paper, using knowledge of marginal likelihood and marginal distribution, the optimized strategy of marginal based ontology sparse vector learning algorithm is presented. Finally, the new algorithm is applied to gene ontology and plant ontology to verify its efficiency.

AUC is an important performance measure that has been used in diverse tasks, such as class-imbalanced learning, cost-sensitive learning, learning to rank, etc. In this work, we focus on one-pass AUC optimization that requires going through training data only once without having to store the entire training dataset. Conventional online learning algorithms cannot be applied directly to one-pass AUC optimization because AUC is measured by a sum of losses defined over pairs of instances from different classes. We develop a regression-based algorithm which only needs to maintain the first and second-order statistics of training data in memory, resulting in a storage requirement independent of the number of training data. To efficiently handle high-dimensional data, we develop two deterministic algorithms that approximate the covariance matrices. We verify, both theoretically and empirically, the effectiveness of the proposed algorithms.

© 2019 Elsevier Ltd This paper presents a machine learning aided stochastic free vibration analysis for functionally graded (FG) bar-type structures through finite element method (FEM). The considered system uncertainties including the constituent material properties, the dimensions of structural members, and the degree of the gradation of the FGM are incorporated. A novel kernel-based machine learning technique, namely the extended support vector regression (X-SVR), is presented to estimate the governing relationship between the uncertain system parameters and the structural natural frequencies. Subsequently, by applying the Monte-Carlo Simulation (MCS) through the established regression model, various types of statistical characteristics (i.e., mean, standard deviation, probability density function or PDF, and cumulative distribution function or CDF) of structural natural frequencies can be effectively established. Four numerical examples including test functions and practically stimulated engineering structures are thoroughly investigated herein to demonstrate the accuracy, applicability, and computational efficiency of the proposed approach.

Having a reliable approximation of heating load (HL) and cooling load (CL) is a substantial task for evaluating the energy performance of buildings (EPB). Also, the appearance of soft computing techniques has made many traditional methods antiquated. Thus, the main effort of this study was to evaluate the capability of several learning methods for appraising the HL and CL of a residential building. To this end, a proper dataset consisting of eight influential factors was provided. To simplify the problem, we executed feature validity by using a correlation-based feature subset selection (CfsSubsetEval) technique. The results of this process showed that wall area, overall height, orientation and glazing area have the most significant impact on the HL and CL simulation. After preparing the suitable dataset, sixteen learning methods namely, elastic net (EN), Gaussian process regression (GPR), least median of squares regression (LMSR), multiple linear regression (MLR), multi-layer perceptron regression (MPR), multi-layer perceptron (MLP), radial basis function regression (RBFR), sequential minimal optimization regression (SMOR), functions XNV, lazy K-star, lazy LWL, rules decision table (RDT), M5Rules, alternating model tree (AMT), directional path consistency (DPC), and Random Forest (RF) were developed in Weka environment to forecast the HL and CL variables. Referring to the results, it was concluded that RF, lazy K-star, RDT and AMT outperform other predictive models. Also, comparing the results with the results of the previous studies showed that the applied feature reduction not only did not disturb the learning process but also has enhanced the performance of models. Also, due to the excellent accuracy of the MLP, a formula was derived from the optimized structure of it to predict the HL and CL variables.

Deep neural networks have witnessed great successes in various real applications, but it requires a large number of labeled data for training. In this paper, we propose tri-net, a deep neural network which is able to use massive unlabeled data to help learning with limited labeled data. We consider model initialization, diversity augmentation and pseudo-label editing simultaneously. In our work, we utilize output smearing to initialize modules, use fine-tuning on labeled data to augment diversity and eliminate unstable pseudo-labels to alleviate the influence of suspicious pseudo-labeled data. Experiments show that our method achieves the best performance in comparison with state-of-the-art semi-supervised deep learning methods. In particular, it achieves 8.30% error rate on CIFAR-10 by using only 4000 labeled examples.

The functional connectome has gained increased attention in the neuroscience community. In general, most network connectivity models are based on correlations between discrete-time series signals that only connect two different brain regions. However, these bivariate region-to-region models do not involve three or more brain regions that form a subnetwork. Here we propose a learning-based method to explore subnetwork biomarkers that are significantly distinguishable between two clinical cohorts. Learning on hypergraph is employed in our work. Specifically, we construct a hypergraph by exhaustively inspecting all possible subnetworks for all subjects, where each hyperedge connects a group of subjects demonstrating highly correlated functional connectivity behavior throughout the underlying subnetwork. The objective function of hypergraph learning is to jointly optimize the weights for all hyperedges which make the separation of two groups by the learned data representation be in the best consensus with the observed clinical labels. We deploy our method to find high order childhood autism biomarkers from rs-fMRI images. Promising results have been obtained from comprehensive evaluation on the discriminative power and generality in diagnosis of Autism.

Automatic rumor detection is technically very challenging. In this work, we try to learn discriminative features from tweets content by following their non-sequential propagation structure and generate more powerful representations for identifying different type of rumors. We propose two recursive neural models based on a bottom-up and a top-down tree-structured neural networks for rumor representation learning and classification, which naturally conform to the propagation layout of tweets. Results on two public Twitter datasets demonstrate that our recursive neural models 1) achieve much better performance than state-of-the-art approaches; 2) demonstrate superior capacity on detecting rumors at very early stage.

In this paper, a joint machine learning and game theory modeling (MLGT) framework is proposed for inter frame coding tree unit (CTU) level bit allocation and rate control (RC) optimization in high efficiency video coding (HEVC). First, a support vector machine-based multi-classification scheme is proposed to improve the prediction accuracy of CTU-level rate-distortion (R-D) model. The legacy “chicken-and-egg” dilemma in video coding is proposed to be overcome by the learning-based R-D model. Second, a mixed R-D model-based cooperative bargaining game theory is proposed for bit allocation optimization, where the convexity of the mixed R-D model-based utility function is proved, and Nash bargaining solution is achieved by the proposed iterative solution search method. The minimum utility is adjusted by the reference coding distortion and frame-level quantization parameter (QP) change. Finally, intra frame QP and inter frame adaptive bit ratios are adjusted to make inter frames have more bit resources to maintain smooth quality and bit consumption in the bargaining game optimization. Experimental results demonstrate that the proposed MLGT-based RC method can achieve much better R-D performances, quality smoothness, bit rate accuracy, buffer control results, and subjective visual quality than the other state-of-the-art one-pass RC methods, and the achieved R-D performances are very close to the performance limits from the FixedQP method.

In this article, we present a sparse vector learning algorithm for ontology similarity measure and ontology mapping by virtue of accelerated first-order technology. The main procedure of our iterative algorithm is relying on prox- imity operator computation, Picard-Opial process and accelerated first-order tricks. The simulation experimental results show that the new proposed algorithm has high efficiency and accuracy in ontology similarity measure and ontology mapping in plant science and university application.

Rockburst is an important accident scenario in deep underground engineering. Because there are numerous, complicated factors that lead to rockbursts, their forecasting is a difficult task, which, based on an engineering analogy and geological analysis, requires the use of clustering methods in rockburst forecasts. Because the environmental causes of rockbursts are complicated, this clustering problem makes for a complicated random optimization problem (that is also a fuzzy optimization problem) that cannot be solved in a satisfactory manner using traditional methods. To improve the computational efficiency and accuracy of the traditional ant colony clustering algorithm, an abstraction ant colony clustering algorithm using a data combination mechanism is proposed. Based on an analysis of rockburst sample data and using an engineering analogy thinking by the abstraction ant colony clustering algorithm, a new method for forecasting rockbursts in deep underground engineering is proposed. A set of common engineering examples are used to verify the new algorithm. The engineering applications prove that, compared with the traditional ant colony clustering algorithm and based on their similar computational difficulty and complexity, the abstraction ant colony clustering algorithm produces results that are not only more accurate but are also determined more efficiently. As the complexity of the problem increases, the algorithm’s computational efficiency increases. In other words, the more complicated the problem is, the more efficient the algorithm becomes. Thus, the abstraction ant colony clustering algorithm is well suited to large complicated engineering problems.