다중혈관 관상동맥 환자에서 y-문합을 이용하여 양쪽 내흉동맥만을 사용한 우회술의 조기 성적

[신간의 별자리x] 우리/미술, 그리고 ‘슬픔의 박물관’

Probability Distributions.- Linear Models for Regression.- Linear Models for Classification.- Neural Networks.- Kernel Methods.- Sparse Kernel Machines.- Graphical Models.- Mixture Models and EM.- Approximate Inference.- Sampling Methods.- Continuous Latent Variables.- Sequential Data.- Combining Models.

Pattern Recognition and Machine Learning

Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.

/pdf/anomaly-detection-a-survey-1x33g9m0a3.pdf

Anomaly detection: A survey

Data Mining - Concepts and Techniques.

Emotion cause analysis has been a key topic in natural language processing. Existing methods ignore the contexts around the emotion word which can provide an emotion cause clue. Meanwhile, the clauses in a document play different roles on stimulating a certain emotion, depending on their content relevance. Therefore, we propose a co-attention neural network model for emotion cause analysis with emotional context awareness. The method encodes the clauses with a co-attention based bi-directional long short-term memory into high-level input representations, which are further fed into a convolutional layer for emotion cause analysis. Experimental results show that our approach outperforms the state-of-the-art baseline methods.

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness

Emotion cause analysis has elicited wide interest in both academia and industry, and aims to identify the reasons behind certain emotions expressed in text. Most of the current studies on emotion cause analysis do not consider two types of information: i) the context of the emotional word, which can provide rich emotional details, and ii) the interaction between the candidate clause and the emotional clause (containing the emotional word). The above information is able to provide important clues in emotion cause analysis. In this paper, we propose a multi-attention-based neural network model to address this issue. First, our model encodes the clause via bidirectional long short-term memory, which can incorporate the contextual information of the word. Second, a multi-attention mechanism is designed to capture the mutual influences between the emotion clause and each candidate clause, and then generate the representations for the above two clauses separately. With this design, our model creates better-distributed representations of the emotion expressions and clauses. Finally, these representations are fed into a convolutional neural network to model the emotion cause clause. The experimental results show that our proposed approach outperforms the state-of-the-art baseline methods by a significant margin.

Context-aware emotion cause analysis with multi-attention-based neural network

Compared with single-modal content, multimodal data can express users’ feelings and sentiments more vividly and interestingly. Therefore, multimodal sentiment analysis has become a popular research topic. However, most existing methods either learn modal sentiment feature independently, without considering their correlations, or they simply integrate multimodal features. In addition, most publicly available multimodal datasets are labeled by sentiment polarities, while the emotions expressed by users are specific. Based on this observation, in this paper, we build a large-scale image-text emotion dataset (i.e., labeled by different emotions), called TumEmo, with more than 190,000 instances from Tumblr.1 We further propose a novel multimodal emotion analysis model based on the Multi-view Attentional Network (MVAN), which utilizes a memory network that is continually updated to obtain the deep semantic features of image-text. The model includes three stages: feature mapping, interactive learning, and feature fusion. In the feature mapping stage, we leverage image features from an object viewpoint and a scene viewpoint to capture effective information for multimodal emotion analysis. Then, an interactive learning mechanism is adopted that uses the memory network; this mechanism extracts single-modal emotion features and interactively models the cross-view dependencies between the image and text. In the feature fusion stage, multiple features are deeply fused using a multilayer perceptron and a stacking-pooling module. The experimental results on the MVSA-Single, MVSA-Multiple, and TumEmo datasets show that the proposed MVAN outperforms strong baseline models by large margins.

Image-Text Multimodal Emotion Classification via Multi-View Attentional Network

Analyzing sentiment polarities of microblog has become a hot research topic for both academic and industrial communities. Most of the existing algorithms regard each microblog as an independent training instance. However, the sentiments embedded in short tweets are usually ambiguous and context-aware. Even a non-sentiment word might convey a clear emotional tendency in certain microblog conversations. In this paper, we regard the microblog conversation as sequence, and develop a Context Attention based Long Short-Term Memory (CA-LSTM) network to incorporate preceding tweets for context-aware sentiment classification. The CA-LSTM network has a hierarchial structure for modeling microblog sequence and allocates the words and tweets with different weights using attention mechanism. Our proposed method can not only alleviate the sparsity problem in feature space, but also capture long distance sentiment context dependency in microblog conversations. Experimental evaluations on a public available dataset show that the proposed CA-LSTM network with context information can outperform other strong baselines by a large margin.

Attention based hierarchical LSTM network for context-aware microblog sentiment classification

Recently, blogs have emerged as the major platform for people to express their feelings and sentiments in the age of Web 2.0. The common emotions, which reflect people’s collective and overall sentiments, are becoming the major concern for governments, business companies and individual users. Different from previous literatures on sentiment classification and summarization, the major issue of common emotion extraction is to find out people’s collective sentiments and their corresponding distributions on the Web. Most existing blog clustering methods take into account keywords, stories or timelines but neglect the embedded sentiments, which are considered very important features of blogs. In this paper, a novel method based on Probabilistic Latent Semantic Analysis (PLSA) is presented to model the hidden sentiment factors and an emotion-oriented clustering approach is proposed to find common emotions according to the fine-grained sentiment similarity between blogs. Extensive experiments are conducted on real-world datasets consisting of different topics. The results show that our approach can partition blogs into sentiment coherent clusters and the extracted common emotion words afford good navigation guidelines for embedded sentiments in each cluster.

https://ink.library.smu.edu.sg/cgi/viewcontent.cgi?article=5554&context=sis_research

Daling Wang

Papers

A Co-Attention Neural Network Model for Emotion Cause Analysis with Emotional Context Awareness

Context-aware emotion cause analysis with multi-attention-based neural network

Image-Text Multimodal Emotion Classification via Multi-View Attentional Network

Attention based hierarchical LSTM network for context-aware microblog sentiment classification

Extracting common emotions from blogs based on fine-grained sentiment clustering