Paper Group ANR 442
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion. Using Deep Networks for Drone Detection. Can the early human visual system compete with Deep Neural Networks?. Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection. Learning Linear Dynamical Systems via Spectra …
Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion
| Title | Argument Strength is in the Eye of the Beholder: Audience Effects in Persuasion |
| Authors | Stephanie M. Lukin, Pranav Anand, Marilyn Walker, Steve Whittaker |
| Abstract | Americans spend about a third of their time online, with many participating in online conversations on social and political issues. We hypothesize that social media arguments on such issues may be more engaging and persuasive than traditional media summaries, and that particular types of people may be more or less convinced by particular styles of argument, e.g. emotional arguments may resonate with some personalities while factual arguments resonate with others. We report a set of experiments testing at large scale how audience variables interact with argument style to affect the persuasiveness of an argument, an under-researched topic within natural language processing. We show that belief change is affected by personality factors, with conscientious, open and agreeable people being more convinced by emotional arguments. |
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| Published | 2017-08-30 |
| URL | http://arxiv.org/abs/1708.09085v1 |
| http://arxiv.org/pdf/1708.09085v1.pdf | |
| PWC | https://paperswithcode.com/paper/argument-strength-is-in-the-eye-of-the |
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Using Deep Networks for Drone Detection
| Title | Using Deep Networks for Drone Detection |
| Authors | Cemal Aker, Sinan Kalkan |
| Abstract | Drone detection is the problem of finding the smallest rectangle that encloses the drone(s) in a video sequence. In this study, we propose a solution using an end-to-end object detection model based on convolutional neural networks. To solve the scarce data problem for training the network, we propose an algorithm for creating an extensive artificial dataset by combining background-subtracted real images. With this approach, we can achieve precision and recall values both of which are high at the same time. |
| Tasks | Object Detection |
| Published | 2017-06-18 |
| URL | http://arxiv.org/abs/1706.05726v1 |
| http://arxiv.org/pdf/1706.05726v1.pdf | |
| PWC | https://paperswithcode.com/paper/using-deep-networks-for-drone-detection |
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Can the early human visual system compete with Deep Neural Networks?
| Title | Can the early human visual system compete with Deep Neural Networks? |
| Authors | Samuel Dodge, Lina Karam |
| Abstract | We study and compare the human visual system and state-of-the-art deep neural networks on classification of distorted images. Different from previous works, we limit the display time to 100ms to test only the early mechanisms of the human visual system, without allowing time for any eye movements or other higher level processes. Our findings show that the human visual system still outperforms modern deep neural networks under blurry and noisy images. These findings motivate future research into developing more robust deep networks. |
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| Published | 2017-10-12 |
| URL | http://arxiv.org/abs/1710.04744v1 |
| http://arxiv.org/pdf/1710.04744v1.pdf | |
| PWC | https://paperswithcode.com/paper/can-the-early-human-visual-system-compete |
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Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection
| Title | Weakly Supervised Cross-Lingual Named Entity Recognition via Effective Annotation and Representation Projection |
| Authors | Jian Ni, Georgiana Dinu, Radu Florian |
| Abstract | The state-of-the-art named entity recognition (NER) systems are supervised machine learning models that require large amounts of manually annotated data to achieve high accuracy. However, annotating NER data by human is expensive and time-consuming, and can be quite difficult for a new language. In this paper, we present two weakly supervised approaches for cross-lingual NER with no human annotation in a target language. The first approach is to create automatically labeled NER data for a target language via annotation projection on comparable corpora, where we develop a heuristic scheme that effectively selects good-quality projection-labeled data from noisy data. The second approach is to project distributed representations of words (word embeddings) from a target language to a source language, so that the source-language NER system can be applied to the target language without re-training. We also design two co-decoding schemes that effectively combine the outputs of the two projection-based approaches. We evaluate the performance of the proposed approaches on both in-house and open NER data for several target languages. The results show that the combined systems outperform three other weakly supervised approaches on the CoNLL data. |
| Tasks | Named Entity Recognition, Word Embeddings |
| Published | 2017-07-08 |
| URL | http://arxiv.org/abs/1707.02483v1 |
| http://arxiv.org/pdf/1707.02483v1.pdf | |
| PWC | https://paperswithcode.com/paper/weakly-supervised-cross-lingual-named-entity |
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Learning Linear Dynamical Systems via Spectral Filtering
| Title | Learning Linear Dynamical Systems via Spectral Filtering |
| Authors | Elad Hazan, Karan Singh, Cyril Zhang |
| Abstract | We present an efficient and practical algorithm for the online prediction of discrete-time linear dynamical systems with a symmetric transition matrix. We circumvent the non-convex optimization problem using improper learning: carefully overparameterize the class of LDSs by a polylogarithmic factor, in exchange for convexity of the loss functions. From this arises a polynomial-time algorithm with a near-optimal regret guarantee, with an analogous sample complexity bound for agnostic learning. Our algorithm is based on a novel filtering technique, which may be of independent interest: we convolve the time series with the eigenvectors of a certain Hankel matrix. |
| Tasks | Time Series |
| Published | 2017-11-02 |
| URL | http://arxiv.org/abs/1711.00946v1 |
| http://arxiv.org/pdf/1711.00946v1.pdf | |
| PWC | https://paperswithcode.com/paper/learning-linear-dynamical-systems-via |
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Detecting Hate Speech in Social Media
| Title | Detecting Hate Speech in Social Media |
| Authors | Shervin Malmasi, Marcos Zampieri |
| Abstract | In this paper we examine methods to detect hate speech in social media, while distinguishing this from general profanity. We aim to establish lexical baselines for this task by applying supervised classification methods using a recently released dataset annotated for this purpose. As features, our system uses character n-grams, word n-grams and word skip-grams. We obtain results of 78% accuracy in identifying posts across three classes. Results demonstrate that the main challenge lies in discriminating profanity and hate speech from each other. A number of directions for future work are discussed. |
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| Published | 2017-12-18 |
| URL | http://arxiv.org/abs/1712.06427v2 |
| http://arxiv.org/pdf/1712.06427v2.pdf | |
| PWC | https://paperswithcode.com/paper/detecting-hate-speech-in-social-media |
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A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud
| Title | A region-growing approach for automatic outcrop fracture extraction from a three-dimensional point cloud |
| Authors | Xin Wang, Lejun Zou, Xiaohua Shen, Yupeng Ren, Yi Qin |
| Abstract | Conventional manual surveys of rock mass fractures usually require large amounts of time and labor; yet, they provide a relatively small set of data that cannot be considered representative of the study region. Terrestrial laser scanners are increasingly used for fracture surveys because they can efficiently acquire large area, high-resolution, three-dimensional (3D) point clouds from outcrops. However, extracting fractures and other planar surfaces from 3D outcrop point clouds is still a challenging task. No method has been reported that can be used to automatically extract the full extent of every individual fracture from a 3D outcrop point cloud. In this study, we propose a method using a region-growing approach to address this problem; the method also estimates the orientation of each fracture. In this method, criteria based on the local surface normal and curvature of the point cloud are used to initiate and control the growth of the fracture region. In tests using outcrop point cloud data, the proposed method identified and extracted the full extent of individual fractures with high accuracy. Compared with manually acquired field survey data, our method obtained better-quality fracture data, thereby demonstrating the high potential utility of the proposed method. |
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| Published | 2017-06-27 |
| URL | http://arxiv.org/abs/1707.03266v1 |
| http://arxiv.org/pdf/1707.03266v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-region-growing-approach-for-automatic |
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Conversation Modeling on Reddit using a Graph-Structured LSTM
| Title | Conversation Modeling on Reddit using a Graph-Structured LSTM |
| Authors | Vicky Zayats, Mari Ostendorf |
| Abstract | This paper presents a novel approach for modeling threaded discussions on social media using a graph-structured bidirectional LSTM which represents both hierarchical and temporal conversation structure. In experiments with a task of predicting popularity of comments in Reddit discussions, the proposed model outperforms a node-independent architecture for different sets of input features. Analyses show a benefit to the model over the full course of the discussion, improving detection in both early and late stages. Further, the use of language cues with the bidirectional tree state updates helps with identifying controversial comments. |
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| Published | 2017-04-07 |
| URL | http://arxiv.org/abs/1704.02080v1 |
| http://arxiv.org/pdf/1704.02080v1.pdf | |
| PWC | https://paperswithcode.com/paper/conversation-modeling-on-reddit-using-a-graph |
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Multimodal Classification for Analysing Social Media
| Title | Multimodal Classification for Analysing Social Media |
| Authors | Chi Thang Duong, Remi Lebret, Karl Aberer |
| Abstract | Classification of social media data is an important approach in understanding user behavior on the Web. Although information on social media can be of different modalities such as texts, images, audio or videos, traditional approaches in classification usually leverage only one prominent modality. Techniques that are able to leverage multiple modalities are often complex and susceptible to the absence of some modalities. In this paper, we present simple models that combine information from different modalities to classify social media content and are able to handle the above problems with existing techniques. Our models combine information from different modalities using a pooling layer and an auxiliary learning task is used to learn a common feature space. We demonstrate the performance of our models and their robustness to the missing of some modalities in the emotion classification domain. Our approaches, although being simple, can not only achieve significantly higher accuracies than traditional fusion approaches but also have comparable results when only one modality is available. |
| Tasks | Auxiliary Learning, Emotion Classification |
| Published | 2017-08-07 |
| URL | http://arxiv.org/abs/1708.02099v1 |
| http://arxiv.org/pdf/1708.02099v1.pdf | |
| PWC | https://paperswithcode.com/paper/multimodal-classification-for-analysing |
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Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks
| Title | Urban Land Cover Classification with Missing Data Modalities Using Deep Convolutional Neural Networks |
| Authors | Michael Kampffmeyer, Arnt-Børre Salberg, Robert Jenssen |
| Abstract | Automatic urban land cover classification is a fundamental problem in remote sensing, e.g. for environmental monitoring. The problem is highly challenging, as classes generally have high inter-class and low intra-class variance. Techniques to improve urban land cover classification performance in remote sensing include fusion of data from different sensors with different data modalities. However, such techniques require all modalities to be available to the classifier in the decision-making process, i.e. at test time, as well as in training. If a data modality is missing at test time, current state-of-the-art approaches have in general no procedure available for exploiting information from these modalities. This represents a waste of potentially useful information. We propose as a remedy a convolutional neural network (CNN) architecture for urban land cover classification which is able to embed all available training modalities in a so-called hallucination network. The network will in effect replace missing data modalities in the test phase, enabling fusion capabilities even when data modalities are missing in testing. We demonstrate the method using two datasets consisting of optical and digital surface model (DSM) images. We simulate missing modalities by assuming that DSM images are missing during testing. Our method outperforms both standard CNNs trained only on optical images as well as an ensemble of two standard CNNs. We further evaluate the potential of our method to handle situations where only some DSM images are missing during testing. Overall, we show that we can clearly exploit training time information of the missing modality during testing. |
| Tasks | Decision Making |
| Published | 2017-09-21 |
| URL | http://arxiv.org/abs/1709.07383v2 |
| http://arxiv.org/pdf/1709.07383v2.pdf | |
| PWC | https://paperswithcode.com/paper/urban-land-cover-classification-with-missing |
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DeepSkeleton: Skeleton Map for 3D Human Pose Regression
| Title | DeepSkeleton: Skeleton Map for 3D Human Pose Regression |
| Authors | Qingfu Wan, Wei Zhang, Xiangyang Xue |
| Abstract | Despite recent success on 2D human pose estimation, 3D human pose estimation still remains an open problem. A key challenge is the ill-posed depth ambiguity nature. This paper presents a novel intermediate feature representation named skeleton map for regression. It distills structural context from irrelavant properties of RGB image e.g. illumination and texture. It is simple, clean and can be easily generated via deconvolution network. For the first time, we show that training regression network from skeleton map alone is capable of meeting the performance of state-of-theart 3D human pose estimation works. We further exploit the power of multiple 3D hypothesis generation to obtain reasonbale 3D pose in consistent with 2D pose detection. The effectiveness of our approach is validated on challenging in-the-wild dataset MPII and indoor dataset Human3.6M. |
| Tasks | 3D Human Pose Estimation, Pose Estimation |
| Published | 2017-11-29 |
| URL | http://arxiv.org/abs/1711.10796v1 |
| http://arxiv.org/pdf/1711.10796v1.pdf | |
| PWC | https://paperswithcode.com/paper/deepskeleton-skeleton-map-for-3d-human-pose |
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Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network
| Title | Disguised Face Identification (DFI) with Facial KeyPoints using Spatial Fusion Convolutional Network |
| Authors | Amarjot Singh, Devendra Patil, G Meghana Reddy, SN Omkar |
| Abstract | Disguised face identification (DFI) is an extremely challenging problem due to the numerous variations that can be introduced using different disguises. This paper introduces a deep learning framework to first detect 14 facial key-points which are then utilized to perform disguised face identification. Since the training of deep learning architectures relies on large annotated datasets, two annotated facial key-points datasets are introduced. The effectiveness of the facial keypoint detection framework is presented for each keypoint. The superiority of the key-point detection framework is also demonstrated by a comparison with other deep networks. The effectiveness of classification performance is also demonstrated by comparison with the state-of-the-art face disguise classification methods. |
| Tasks | Face Identification, Keypoint Detection |
| Published | 2017-08-30 |
| URL | http://arxiv.org/abs/1708.09317v1 |
| http://arxiv.org/pdf/1708.09317v1.pdf | |
| PWC | https://paperswithcode.com/paper/disguised-face-identification-dfi-with-facial |
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A Measure for Dialog Complexity and its Application in Streamlining Service Operations
| Title | A Measure for Dialog Complexity and its Application in Streamlining Service Operations |
| Authors | Q Vera Liao, Biplav Srivastava, Pavan Kapanipathi |
| Abstract | Dialog is a natural modality for interaction between customers and businesses in the service industry. As customers call up the service provider, their interactions may be routine or extraordinary. We believe that these interactions, when seen as dialogs, can be analyzed to obtain a better understanding of customer needs and how to efficiently address them. We introduce the idea of a dialog complexity measure to characterize multi-party interactions, propose a general data-driven method to calculate it, use it to discover insights in public and enterprise dialog datasets, and demonstrate its beneficial usage in facilitating better handling of customer requests and evaluating service agents. |
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| Published | 2017-08-04 |
| URL | http://arxiv.org/abs/1708.04134v1 |
| http://arxiv.org/pdf/1708.04134v1.pdf | |
| PWC | https://paperswithcode.com/paper/a-measure-for-dialog-complexity-and-its |
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Neighborhood-Based Label Propagation in Large Protein Graphs
| Title | Neighborhood-Based Label Propagation in Large Protein Graphs |
| Authors | Sabeur Aridhi, Seyed Ziaeddin Alborzi, Malika Smaïl-Tabbone, Marie-Dominique Devignes, David Ritchie |
| Abstract | Understanding protein function is one of the keys to understanding life at the molecular level. It is also important in several scenarios including human disease and drug discovery. In this age of rapid and affordable biological sequencing, the number of sequences accumulating in databases is rising with an increasing rate. This presents many challenges for biologists and computer scientists alike. In order to make sense of this huge quantity of data, these sequences should be annotated with functional properties. UniProtKB consists of two components: i) the UniProtKB/Swiss-Prot database containing protein sequences with reliable information manually reviewed by expert bio-curators and ii) the UniProtKB/TrEMBL database that is used for storing and processing the unknown sequences. Hence, for all proteins we have available the sequence along with few more information such as the taxon and some structural domains. Pairwise similarity can be defined and computed on proteins based on such attributes. Other important attributes, while present for proteins in Swiss-Prot, are often missing for proteins in TrEMBL, such as their function and cellular localization. The enormous number of protein sequences now in TrEMBL calls for rapid procedures to annotate them automatically. In this work, we present DistNBLP, a novel Distributed Neighborhood-Based Label Propagation approach for large-scale annotation of proteins. To do this, the functional annotations of reviewed proteins are used to predict those of non-reviewed proteins using label propagation on a graph representation of the protein database. DistNBLP is built on top of the “akka” toolkit for building resilient distributed message-driven applications. |
| Tasks | Drug Discovery |
| Published | 2017-08-09 |
| URL | http://arxiv.org/abs/1708.07074v1 |
| http://arxiv.org/pdf/1708.07074v1.pdf | |
| PWC | https://paperswithcode.com/paper/neighborhood-based-label-propagation-in-large |
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TwiInsight: Discovering Topics and Sentiments from Social Media Datasets
| Title | TwiInsight: Discovering Topics and Sentiments from Social Media Datasets |
| Authors | Zhengkui Wang, Guangdong Bai, Soumyadeb Chowdhury, Quanqing Xu, Zhi Lin Seow |
| Abstract | Social media platforms contain a great wealth of information which provides opportunities for us to explore hidden patterns or unknown correlations, and understand people’s satisfaction with what they are discussing. As one showcase, in this paper, we present a system, TwiInsight which explores the insight of Twitter data. Different from other Twitter analysis systems, TwiInsight automatically extracts the popular topics under different categories (e.g., healthcare, food, technology, sports and transport) discussed in Twitter via topic modeling and also identifies the correlated topics across different categories. Additionally, it also discovers the people’s opinions on the tweets and topics via the sentiment analysis. The system also employs an intuitive and informative visualization to show the uncovered insight. Furthermore, we also develop and compare six most popular algorithms - three for sentiment analysis and three for topic modeling. |
| Tasks | Sentiment Analysis |
| Published | 2017-05-23 |
| URL | http://arxiv.org/abs/1705.08094v1 |
| http://arxiv.org/pdf/1705.08094v1.pdf | |
| PWC | https://paperswithcode.com/paper/twiinsight-discovering-topics-and-sentiments |
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