MICK: A Meta-Learning Framework for Few-shot Relation Classification with Small Training Data

Few-shot relation classification seeks to classify incoming query instances after meeting only few support instances. This ability is gained by training with large amount of in-domain annotated data. In this paper, we tackle an even harder problem by further limiting the amount of data available at training time. We propose a few-shot learning framework for relation classification, which is particularly powerful when the training data is very small. In this framework, models not only strive to classify query instances, but also seek underlying knowledge about the support instances to obtain better instance representations. The framework also includes a method for aggregating cross-domain knowledge into models by open-source task enrichment. Additionally, we construct a brand new dataset: the TinyRel-CM dataset, a few-shot relation classification dataset in health domain with purposely small training data and challenging relation classes. Experimental results demonstrate that our framework brings performance gains for most underlying classification models, outperforms the state-of-the-art results given small training data, and achieves competitive results with sufficiently large training data

AffinityNet: semi-supervised few-shot learning for disease type prediction

While deep learning has achieved great success in computer vision and many other fields, currently it does not work very well on patient genomic data with the "big p, small N" problem (i.e., a relatively small number of samples with high-dimensional features). In order to make deep learning work with a small amount of training data, we have to design new models that facilitate few-shot learning. Here we present the Affinity Network Model (AffinityNet), a data efficient deep learning model that can learn from a limited number of training examples and generalize well. The backbone of the AffinityNet model consists of stacked k-Nearest-Neighbor (kNN) attention pooling layers. The kNN attention pooling layer is a generalization of the Graph Attention Model (GAM), and can be applied to not only graphs but also any set of objects regardless of whether a graph is given or not. As a new deep learning module, kNN attention pooling layers can be plugged into any neural network model just like convolutional layers. As a simple special case of kNN attention pooling layer, feature attention layer can directly select important features that are useful for classification tasks. Experiments on both synthetic data and cancer genomic data from TCGA projects show that our AffinityNet model has better generalization power than conventional neural network models with little training data. The code is freely available at https://github.com/BeautyOfWeb/AffinityNet .Comment: 14 pages, 6 figure

DEVELOPMENT OF LEARNING MODULE MENGOLAH DATA DENGAN MICROSOFT ACCESS 2003 ON COMPUTER SKILLS AND MANAGEMENTINFORMATION LESSONS IN SMK NEGERI 2 SUKOHARJO

This study aims to: 1) Make learning modules process the data with Microsoft Access 2003, 2) Examine the feasibility of learning module to process data with Microsoft Access 2003 as a medium of teaching in SMK Negeri 2 Sukoharjo, and 3) Determine the effectiveness of using learning modules process data with Microsoft Access 2003 on competency achievement data processing applications. This research is a research and development use development model Brog & Gall. Research and development is carried out by five steps: 1) Conducting analysis of products, 2) To develop the initial product, 3) Validation of expert and revision, 4) small-scale field trials and revisions, and 5) large-scale field tests and the final product. The subject of research on small-scale field trials were 12 students and research subjects on a large scale field tests of 78 students with a sampling technique that is purposive sampling. Determination of eligibility is done in an expert validation and small-scale field trials using a questionnaire while the effectiveness is done in large scale field tests using the results of the assessment practices. The data analysis technique for the feasibility of using descriptive statistics while the effectiveness of using two-sample t-test independent. According to expert assessment of materials and media expert in the expert validation, the learning module fit for use while in the small-scale field trials of learning modules fit for use by percentage of 83.33%. The results obtained by t-test t = 24.028 with df = 74 and p = 0.000, so there is a difference between the practicum students who use learning modules that do not use the learning modules. The mean of the lab for a class that uses a module that is 90.618 while the class does not use a module that is 69.405. As a whole class using the modules stated thoroughly in the competence and who do not use the module there are 5 students who need to make improvements

DIY Human Action Data Set Generation

The recent successes in applying deep learning techniques to solve standard computer vision problems has aspired researchers to propose new computer vision problems in different domains. As previously established in the field, training data itself plays a significant role in the machine learning process, especially deep learning approaches which are data hungry. In order to solve each new problem and get a decent performance, a large amount of data needs to be captured which may in many cases pose logistical difficulties. Therefore, the ability to generate de novo data or expand an existing data set, however small, in order to satisfy data requirement of current networks may be invaluable. Herein, we introduce a novel way to partition an action video clip into action, subject and context. Each part is manipulated separately and reassembled with our proposed video generation technique. Furthermore, our novel human skeleton trajectory generation along with our proposed video generation technique, enables us to generate unlimited action recognition training data. These techniques enables us to generate video action clips from an small set without costly and time-consuming data acquisition. Lastly, we prove through extensive set of experiments on two small human action recognition data sets, that this new data generation technique can improve the performance of current action recognition neural nets