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Graph-based semi-supervised dimension reduction can use the inherent graph structure of samples to propagate label information, and has become a hot research field in machine learning. However, most current methods have strict linear constraints and cannot handle data sampled from nonlinear manifolds; and rely on predefined graphs, which cannot capture the local structure information of data and cannot handle complex non-Gaussian data. To address these issues, this paper proposes a new locality-preserved flexible dimension reduction framework, called Semi-supervised Flexible Adaptive Graph Embedding (SFAG), which learns the embedding space that can preserve the local neighborhood structure by constructing a k 1 -nearest neighbor graph over labeled samples. Then, another k 2 -nearest neighbor graph is constructed on all samples to adaptively construct the optimal graph, clustering labeled and unlabeled embedding sample points with neighborhood relations into the same sub-manifold sharing the same label information. Last but not least, the hard linear projection constraint is relaxed by adding residual terms to obtain not only the nonlinear embedding of the training samples, but also the linear projection matrix applied directly to the out-of-sample. In addition, two different semi-supervised dimension reduction methods for adaptive construction of optimal graphs are proposed based on the SFAG framework. Several evaluation experiments validate the effectiveness of our method in exploring manifold structures and classification tasks. • A novel semi-supervised adaptive flexible dimension reduction method is proposed. • The local and global structure of the data is explored by constructing kNN graphs. • The hard linear constraints on the projection are relaxed by adding a residual term. • An algorithm is designed to solve the norm-constrained problem of feature extraction. [ABSTRACT FROM AUTHOR]

The current demographic development will lead to changing working conditions. The paper at hand presents methods for the investigation of expert knowledge within the company and how to document these information, widely independent from individuals, within a technology based learning environment, using virtual reality (VR) technologies and other media. Expert knowledge is often coded as tacit knowledge [1] and can be hardly verbalized by experts. The extraction of tacit knowledge requires methods that are based on stories. Beyond facts these stories also include tacit knowledge driven by emotions. One of the narrative methods presented in the paper will be the »triad interview« [2]. The documentation of these interviews within texts is not very sustainable and other methods for the documentation of expert knowledge are required. Especially in the field of maintenance that is focused in the paper, virtual reality is a suitable method for the documentation as technicians are familiar with drawings and visual content representations. Virtual reality learning environment can visualize working processes and keep the narrative structure that is required for transferring tacit knowledge. Besides, interactions as a typical characteristic of virtual reality applications allow the design of problem solving tasks. The paper presents the »triad interview« and the »working process analysis« [3] that are used for extracting the relevant information of a working process. For the didactical design of the learning tasks the method of the »complete action« [4] is applied. The paper will present the design of the several phases and their benefit for the learning process. Finally the results and experiences from an evaluation study within the organization will be presented. [ABSTRACT FROM AUTHOR]

Learning useful representations from original features is a key issue in classification tasks. Stacked autoencoders (SAEs) are easy to understand and realize, and they are powerful tools that learn deep features from original features, so they are popular for classification problems. The deep features can further combine the original features to construct more representative features for classification. However, existing SAEs do not consider the original features within the network structure and during training, so the deep features have low complementarity with the original features. To solve the problem, this paper proposes an embedded stacked group sparse autoencoder (ESGSAE) for more effective feature learning. Different from traditional stacked autoencoders, the ESGSAE model considers the complementarity between the original feature and the hidden outputs by embedding the original features into hidden layers. To alleviate the impact of the small sample problem on the generalization of the proposed ESGSAE model, an L 1 regularization-based feature selection strategy is designed to further improve the feature quality. After that, an ensemble model with support vector machine (SVM) and weighted local discriminant preservation projection (w_LPPD) is designed to further enhance the feature quality. Based on the designs above, an embedded stacked group sparse autoencoder ensemble with L 1 regularization and manifold reduction is proposed to obtain deep features with high complementarity in the context of the small sample problem. At the end of this paper, several representative public datasets are used for verification of the proposed algorithm. The results demonstrate that the ESGSAE ensemble model with L 1 regularization and manifold reduction yields superior performance compared to other existing and state-of-the-art feature learning algorithms, including some representative deep stacked autoencoder methods. Specifically, compared with the original features, the representative feature extraction algorithms and the improved autoencoders, the algorithm proposed in this paper can improve the classification accuracy by up to 13.33%, 7.33%, and 9.55%, respectively. The data and codes can be found in: https://share.weiyun.com/Jt7qeORm • A hybrid feature is embedded into the training process to construct a novel deep model. • A group sparsity constraint is introduced to obtain the sparse representations. • The ESGSAE ensemble model is constructed to obtain high complementary features. • A three-step feature learning mechanism is realized. [ABSTRACT FROM AUTHOR]

Relation classification plays an important role in the field of natural language processing (NLP). Previous research on relation classification has verified the effectiveness of using convolutional neural network (CNN) and recurrent neural network (RNN). In this paper, we proposed a model that combine the RNN and CNN (RCNN), which will Give full play to their respective advantages: RNN can learn temporal and context features, especially long-term dependency between two entities, while CNN is capable of catching more potential features. We experiment our model on the SemEval-2010 Task 8 dataset 1 , and the result shows that our method is superior to most of the existing methods. [ABSTRACT FROM AUTHOR]

Clinical narrative text includes information related to a patient's medical history such as chronological progression of medical problems and clinical treatments. A chronological view of a patient's history makes clinical audits easier and improves quality of care. In this paper, we propose a clinical Problem-Action relation extraction method, based on clinical semantic units and event causality patterns, to present a chronological view of a patient's problem and a doctor's action. Based on our observation that a clinical text describes a patient's medical problems and a doctor's treatments in chronological order, a clinical semantic unit is defined as a problem and/or an action relation. Since a clinical event is a basic unit of the problem and action relation, events are extracted from narrative texts, based on the external knowledge resources context features of the conditional random fields. A clinical semantic unit is extracted from each sentence based on time expressions and context structures of events. Then, a clinical semantic unit is classified into a problem and/or action relation based on the event causality patterns of the support vector machines. Experimental results on Korean discharge summaries show 78.8% performance in the F1-measure. This result shows that the proposed method is effectively classifies clinical Problem-Action relations. [ABSTRACT FROM AUTHOR]

In many machine learning algorithms, a major assumption is that the training and the test samples are in the same feature space and have the same distribution. However, for many real applications this assumption does not hold. In this paper, we survey the problem where the training samples and the test samples are from different distributions. This problem can be referred as domain adaptation . The training samples, always with labels, are obtained from what is called source domains, while the test samples, which usually have no labels or only a few labels, are obtained from what is called target domains. The source domains and the target domains are different but related to some extent; the learners can learn some information from the source domains for the learning of the target domains. We focus on the multi-source domain adaptation problem where there is more than one source domain available together with only one target domain. A key issue is how to select good sources and samples for the adaptation. In this survey, we review some theoretical results and well developed algorithms for the multi-source domain adaptation problem. We also discuss some open problems which can be explored in future work. [ABSTRACT FROM AUTHOR]

Despite the benefits of presence, immersive virtual reality is still too limiting and ineffective to be widely adopted in people׳s everyday lives. One important reason for this is its inability to handle highly diverse 3D interaction tasks, such as object manipulation from different scales, perspectives, reference frames, and dimensions. Inspired by preliminary development of an immersive world builder tool, this paper offers an in-depth presentation of our solution to this problem, using a coordinated, tablet- and HMD-based, hybrid virtual environment (HVE) system. Wearing a non-occlusive HMD, the user is able to view and interact with a tablet mounted on the non-dominant forearm, which provides a multi-touch interaction surface, as well as an exocentric God view of the virtual world. To reduce transition gaps across 3D interaction tasks and interfaces, four coordination mechanisms are proposed, two of which were implemented, and one was evaluated in a user study featuring complex level-editing tasks. Based on subjective ratings, task performance, interview feedback, and video analysis, we found that having multiple seamless Interaction Contexts (ICs) with complementary benefits can lead to good performance and user experience, despite the complexity of learning and using the hybrid system. The results also suggest keeping 3DUI tasks synchronized across the ICs, as this can help users understand their relationships, smoothen within- and between-task IC transitions, and inspire more creative use of different interfaces. Finally, based on these findings, a four-step design process is proposed to aid the process of designing and implementing HVE systems, as well as applying them to appropriate task scenarios. [ABSTRACT FROM AUTHOR]

Due to the actual operation of the wind turbine, the collected fault data sets are limited, and it is difficult to realize fault diagnosis through the correlation between variables. To solve this problem, a fault diagnosis method based on virtual expansion and spherical mapping model is proposed in this paper. Firstly, Hermite interpolation is applied to discrete wind power data samples to obtain an interpolation curve about the characteristics of the sample, and a synchronous sampling method is adopted for the interpolation curve to construct a virtual sample. Then, the features of the virtual sample are mapped to a three-dimensional space. Define the spherical data model and perform spherical fitting in a three-dimensional coordinate system. Finally, feature extraction is performed on the fitted spherical surface for training and testing extreme learning machine (ELM). The distribution law of fault data in the spherical model is summarized. Using the data generated based on the Bootstrap method as a control group, comparative experiments were carried out in back-propagation neural network (BP), Probabilistic Neural Network (PNN), General Regression Neural Network (GRNN), and Support Vector Machine (SVM), which verified the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]