Paper Group ANR 722
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining. Unsupervised domain-agnostic identification of product names in social media posts. Learning to Separate Multiple Illuminants in a Single Image. A Machine Learning Approach to Air Traffic Route Choice Modelling. Sequence-to-Sequence Prediction of Vehicle Trajector …
Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining
| Title | Semi-orthogonal Non-negative Matrix Factorization with an Application in Text Mining |
| Authors | Jack Yutong Li, Ruoqing Zhu, Annie Qu, Han Ye, Zhankun Sun |
| Abstract | Emergency Department (ED) crowding is a worldwide issue that affects the efficiency of hospital management and the quality of patient care. This occurs when the request for an admit ward-bed to receive a patient is delayed until an admission decision is made by a doctor. To reduce the overcrowding and waiting time of ED, we build a classifier to predict the disposition of patients using manually-typed nurse notes collected during triage, thereby allowing hospital staff to begin necessary preparation beforehand. However, these triage notes involve high dimensional, noisy, and also sparse text data which makes model fitting and interpretation difficult. To address this issue, we propose the semi-orthogonal non-negative matrix factorization (SONMF) for both continuous and binary design matrices to first bi-cluster the patients and words into a reduced number of topics. The subjects can then be interpreted as a non-subtractive linear combination of orthogonal basis topic vectors. These generated topic vectors provide the hospital with a direct understanding of the cause of admission. We show that by using a transformation of basis, the classification accuracy can be further increased compared to the conventional bag-of-words model and alternative matrix factorization approaches. Through simulated data experiments, we also demonstrate that the proposed method outperforms other non-negative matrix factorization (NMF) methods in terms of factorization accuracy, rate of convergence, and degree of orthogonality. |
| Tasks | Dimensionality Reduction |
| Published | 2018-05-07 |
| URL | https://arxiv.org/abs/1805.02306v3 |
| https://arxiv.org/pdf/1805.02306v3.pdf | |
| PWC | https://paperswithcode.com/paper/semi-orthogonal-non-negative-matrix |
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Unsupervised domain-agnostic identification of product names in social media posts
| Title | Unsupervised domain-agnostic identification of product names in social media posts |
| Authors | Nicolai Pogrebnyakov |
| Abstract | Product name recognition is a significant practical problem, spurred by the greater availability of platforms for discussing products such as social media and product review functionalities of online marketplaces. Customers, product manufacturers and online marketplaces may want to identify product names in unstructured text to extract important insights, such as sentiment, surrounding a product. Much extant research on product name identification has been domain-specific (e.g., identifying mobile phone models) and used supervised or semi-supervised methods. With massive numbers of new products released to the market every year such methods may require retraining on updated labeled data to stay relevant, and may transfer poorly across domains. This research addresses this challenge and develops a domain-agnostic, unsupervised algorithm for identifying product names based on Facebook posts. The algorithm consists of two general steps: (a) candidate product name identification using an off-the-shelf pretrained conditional random fields (CRF) model, part-of-speech tagging and a set of simple patterns; and (b) filtering of candidate names to remove spurious entries using clustering and word embeddings generated from the data. |
| Tasks | Part-Of-Speech Tagging, Word Embeddings |
| Published | 2018-12-11 |
| URL | http://arxiv.org/abs/1812.04662v1 |
| http://arxiv.org/pdf/1812.04662v1.pdf | |
| PWC | https://paperswithcode.com/paper/unsupervised-domain-agnostic-identification |
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Learning to Separate Multiple Illuminants in a Single Image
| Title | Learning to Separate Multiple Illuminants in a Single Image |
| Authors | Zhuo Hui, Ayan Chakrabarti, Kalyan Sunkavalli, Aswin C. Sankaranarayanan |
| Abstract | We present a method to separate a single image captured under two illuminants, with different spectra, into the two images corresponding to the appearance of the scene under each individual illuminant. We do this by training a deep neural network to predict the per-pixel reflectance chromaticity of the scene, which we use in conjunction with a previous flash/no-flash image-based separation algorithm to produce the final two output images. We design our reflectance chromaticity network and loss functions by incorporating intuitions from the physics of image formation. We show that this leads to significantly better performance than other single image techniques and even approaches the quality of the two image separation method. |
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| Published | 2018-11-29 |
| URL | http://arxiv.org/abs/1811.12481v2 |
| http://arxiv.org/pdf/1811.12481v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-separate-multiple-illuminants-in |
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A Machine Learning Approach to Air Traffic Route Choice Modelling
| Title | A Machine Learning Approach to Air Traffic Route Choice Modelling |
| Authors | Rodrigo Marcos, Oliva García-Cantú, Ricardo Herranz |
| Abstract | Air Traffic Flow and Capacity Management (ATFCM) is one of the constituent parts of Air Traffic Management (ATM). The goal of ATFCM is to make airport and airspace capacity meet traffic demand and, when capacity opportunities are exhausted, optimise traffic flows to meet the available capacity. One of the key enablers of ATFCM is the accurate estimation of future traffic demand. The available information (schedules, flight plans, etc.) and its associated level of uncertainty differ across the different ATFCM planning phases, leading to qualitative differences between the types of forecasting that are feasible at each time horizon. While abundant research has been conducted on tactical trajectory prediction (i.e., during the day of operations), trajectory prediction in the pre-tactical phase, when few or no flight plans are available, has received much less attention. As a consequence, the methods currently in use for pre-tactical traffic forecast are still rather rudimentary, often resulting in suboptimal ATFCM decision making. This paper proposes a machine learning approach for the prediction of airlines route choices between two airports as a function of route characteristics, such as flight efficiency, air navigation charges and expected level of congestion. Different predictive models based on multinomial logistic regression and decision trees are formulated and calibrated with historical traffic data, and a critical evaluation of each model is conducted. We analyse the predictive power of each model in terms of its ability to forecast traffic volumes at the level of charging zones, proving significant potential to enhance pre-tactical traffic forecast. We conclude by discussing the limitations and room for improvement of the proposed approach, as well as the future developments required to produce reliable traffic forecasts at a higher spatial and temporal resolution. |
| Tasks | Decision Making, Trajectory Prediction |
| Published | 2018-02-19 |
| URL | http://arxiv.org/abs/1802.06588v2 |
| http://arxiv.org/pdf/1802.06588v2.pdf | |
| PWC | https://paperswithcode.com/paper/a-machine-learning-approach-to-air-traffic |
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Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture
| Title | Sequence-to-Sequence Prediction of Vehicle Trajectory via LSTM Encoder-Decoder Architecture |
| Authors | Seong Hyeon Park, ByeongDo Kim, Chang Mook Kang, Chung Choo Chung, Jun Won Choi |
| Abstract | In this paper, we propose a deep learning based vehicle trajectory prediction technique which can generate the future trajectory sequence of surrounding vehicles in real time. We employ the encoder-decoder architecture which analyzes the pattern underlying in the past trajectory using the long short-term memory (LSTM) based encoder and generates the future trajectory sequence using the LSTM based decoder. This structure produces the $K$ most likely trajectory candidates over occupancy grid map by employing the beam search technique which keeps the $K$ locally best candidates from the decoder output. The experiments conducted on highway traffic scenarios show that the prediction accuracy of the proposed method is significantly higher than the conventional trajectory prediction techniques. |
| Tasks | Trajectory Prediction |
| Published | 2018-02-18 |
| URL | http://arxiv.org/abs/1802.06338v3 |
| http://arxiv.org/pdf/1802.06338v3.pdf | |
| PWC | https://paperswithcode.com/paper/sequence-to-sequence-prediction-of-vehicle |
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On Matching Faces with Alterations due to Plastic Surgery and Disguise
| Title | On Matching Faces with Alterations due to Plastic Surgery and Disguise |
| Authors | Saksham Suri, Anush Sankaran, Mayank Vatsa, Richa Singh |
| Abstract | Plastic surgery and disguise variations are two of the most challenging co-variates of face recognition. The state-of-art deep learning models are not sufficiently successful due to the availability of limited training samples. In this paper, a novel framework is proposed which transfers fundamental visual features learnt from a generic image dataset to supplement a supervised face recognition model. The proposed algorithm combines off-the-shelf supervised classifier and a generic, task independent network which encodes information related to basic visual cues such as color, shape, and texture. Experiments are performed on IIITD plastic surgery face dataset and Disguised Faces in the Wild (DFW) dataset. Results showcase that the proposed algorithm achieves state of the art results on both the datasets. Specifically on the DFW database, the proposed algorithm yields over 87% verification accuracy at 1% false accept rate which is 53.8% better than baseline results computed using VGGFace. |
| Tasks | Face Recognition |
| Published | 2018-11-18 |
| URL | http://arxiv.org/abs/1811.07318v1 |
| http://arxiv.org/pdf/1811.07318v1.pdf | |
| PWC | https://paperswithcode.com/paper/on-matching-faces-with-alterations-due-to |
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Classifier Ensembles for Dialect and Language Variety Identification
| Title | Classifier Ensembles for Dialect and Language Variety Identification |
| Authors | Liviu P. Dinu, Alina Maria Ciobanu, Marcos Zampieri, Shervin Malmasi |
| Abstract | In this paper we present ensemble-based systems for dialect and language variety identification using the datasets made available by the organizers of the VarDial Evaluation Campaign 2018. We present a system developed to discriminate between Flemish and Dutch in subtitles and a system trained to discriminate between four Arabic dialects: Egyptian, Levantine, Gulf, North African, and Modern Standard Arabic in speech broadcasts. Finally, we compare the performance of these two systems with the other systems submitted to the Discriminating between Dutch and Flemish in Subtitles (DFS) and the Arabic Dialect Identification (ADI) shared tasks at VarDial 2018. |
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| Published | 2018-08-14 |
| URL | http://arxiv.org/abs/1808.04800v1 |
| http://arxiv.org/pdf/1808.04800v1.pdf | |
| PWC | https://paperswithcode.com/paper/classifier-ensembles-for-dialect-and-language |
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CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient
| Title | CG-DIQA: No-reference Document Image Quality Assessment Based on Character Gradient |
| Authors | Hongyu Li, Fan Zhu, Junhua Qiu |
| Abstract | Document image quality assessment (DIQA) is an important and challenging problem in real applications. In order to predict the quality scores of document images, this paper proposes a novel no-reference DIQA method based on character gradient, where the OCR accuracy is used as a ground-truth quality metric. Character gradient is computed on character patches detected with the maximally stable extremal regions (MSER) based method. Character patches are essentially significant to character recognition and therefore suitable for use in estimating document image quality. Experiments on a benchmark dataset show that the proposed method outperforms the state-of-the-art methods in estimating the quality score of document images. |
| Tasks | Image Quality Assessment, Optical Character Recognition |
| Published | 2018-07-11 |
| URL | http://arxiv.org/abs/1807.04047v1 |
| http://arxiv.org/pdf/1807.04047v1.pdf | |
| PWC | https://paperswithcode.com/paper/cg-diqa-no-reference-document-image-quality |
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Underwater Image Haze Removal and Color Correction with an Underwater-ready Dark Channel Prior
| Title | Underwater Image Haze Removal and Color Correction with an Underwater-ready Dark Channel Prior |
| Authors | Tomasz Łuczyński, Andreas Birk |
| Abstract | Underwater images suffer from extremely unfavourable conditions. Light is heavily attenuated and scattered. Attenuation creates change in hue, scattering causes so called veiling light. General state of the art methods for enhancing image quality are either unreliable or cannot be easily used in underwater operations. On the other hand there is a well known method for haze removal in air, called Dark Channel Prior. Even though there are known adaptations of this method to underwater applications, they do not always work correctly. This work elaborates and improves upon the initial concept presented in [1]. A modification to the Dark Channel Prior is proposed that allows for an easy application to underwater images. It is also shown that our method outperforms competing solutions based on the Dark Channel Prior. Experiments on real-life data collected within the DexROV project are also presented, showing the robustness and high performance of the proposed algorithm. |
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| Published | 2018-07-11 |
| URL | http://arxiv.org/abs/1807.04169v1 |
| http://arxiv.org/pdf/1807.04169v1.pdf | |
| PWC | https://paperswithcode.com/paper/underwater-image-haze-removal-and-color |
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Orthogonal Matching Pursuit for Text Classification
| Title | Orthogonal Matching Pursuit for Text Classification |
| Authors | Konstantinos Skianis, Nikolaos Tziortziotis, Michalis Vazirgiannis |
| Abstract | In text classification, the problem of overfitting arises due to the high dimensionality, making regularization essential. Although classic regularizers provide sparsity, they fail to return highly accurate models. On the contrary, state-of-the-art group-lasso regularizers provide better results at the expense of low sparsity. In this paper, we apply a greedy variable selection algorithm, called Orthogonal Matching Pursuit, for the text classification task. We also extend standard group OMP by introducing overlapping Group OMP to handle overlapping groups of features. Empirical analysis verifies that both OMP and overlapping GOMP constitute powerful regularizers, able to produce effective and very sparse models. Code and data are available online: https://github.com/y3nk0/OMP-for-Text-Classification . |
| Tasks | Text Classification |
| Published | 2018-07-12 |
| URL | http://arxiv.org/abs/1807.04715v2 |
| http://arxiv.org/pdf/1807.04715v2.pdf | |
| PWC | https://paperswithcode.com/paper/orthogonal-matching-pursuit-for-text |
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Learning to Predict the Cosmological Structure Formation
| Title | Learning to Predict the Cosmological Structure Formation |
| Authors | Siyu He, Yin Li, Yu Feng, Shirley Ho, Siamak Ravanbakhsh, Wei Chen, Barnabás Póczos |
| Abstract | Matter evolved under influence of gravity from minuscule density fluctuations. Non-perturbative structure formed hierarchically over all scales, and developed non-Gaussian features in the Universe, known as the Cosmic Web. To fully understand the structure formation of the Universe is one of the holy grails of modern astrophysics. Astrophysicists survey large volumes of the Universe and employ a large ensemble of computer simulations to compare with the observed data in order to extract the full information of our own Universe. However, to evolve trillions of galaxies over billions of years even with the simplest physics is a daunting task. We build a deep neural network, the Deep Density Displacement Model (hereafter D$^3$M), to predict the non-linear structure formation of the Universe from simple linear perturbation theory. Our extensive analysis, demonstrates that D$^3$M outperforms the second order perturbation theory (hereafter 2LPT), the commonly used fast approximate simulation method, in point-wise comparison, 2-point correlation, and 3-point correlation. We also show that D$^3$M is able to accurately extrapolate far beyond its training data, and predict structure formation for significantly different cosmological parameters. Our study proves, for the first time, that deep learning is a practical and accurate alternative to approximate simulations of the gravitational structure formation of the Universe. |
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| Published | 2018-11-15 |
| URL | https://arxiv.org/abs/1811.06533v2 |
| https://arxiv.org/pdf/1811.06533v2.pdf | |
| PWC | https://paperswithcode.com/paper/learning-to-predict-the-cosmological |
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Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks
| Title | Use Dimensionality Reduction and SVM Methods to Increase the Penetration Rate of Computer Networks |
| Authors | Amir Moradibaad, Ramin Jalilian Mashhoud |
| Abstract | In the world today computer networks have a very important position and most of the urban and national infrastructure as well as organizations are managed by computer networks, therefore, the security of these systems against the planned attacks is of great importance. Therefore, researchers have been trying to find these vulnerabilities so that after identifying ways to penetrate the system, they will provide system protection through preventive or countermeasures. SVM is one of the major algorithms for intrusion detection. In this research, we studied a variety of malware and methods of intrusion detection, provide an efficient method for detecting attacks and utilizing dimension reduction.Thus, we will be able to detect attacks by carefully combining these two algorithms and pre-processes that are performed before the two on the input data. The main question raised is how we can identify attacks on computer networks with the above-mentioned method. In anomalies diagnostic method, by identifying behavior as a normal behavior for the user, the host, or the whole system, any deviation from this behavior is considered as an abnormal behavior, which can be a potential occurrence of an attack. The network intrusion detection system is used by anomaly detection method that uses the SVM algorithm for classification and SVD to reduce the size. Steps of the proposed method include pre-processing of the data set, feature selection, support vector machine, and evaluation.The NSL-KDD data set has been used to teach and test the proposed model. In this study, we inferred the intrusion detection using the SVM algorithm for classification and SVD for diminishing dimensions with no classification algorithm.Also the KNN algorithm has been compared in situations with and without diminishing dimensions,the results have shown that the proposed method has a better performance than comparable methods. |
| Tasks | Anomaly Detection, Dimensionality Reduction, Feature Selection, Intrusion Detection, Network Intrusion Detection |
| Published | 2018-12-07 |
| URL | http://arxiv.org/abs/1812.03173v2 |
| http://arxiv.org/pdf/1812.03173v2.pdf | |
| PWC | https://paperswithcode.com/paper/use-dimensionality-reduction-and-svm-methods |
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Chat-crowd: A Dialog-based Platform for Visual Layout Composition
| Title | Chat-crowd: A Dialog-based Platform for Visual Layout Composition |
| Authors | Paola Cascante-Bonilla, Xuwang Yin, Vicente Ordonez, Song Feng |
| Abstract | In this paper we introduce Chat-crowd, an interactive environment for visual layout composition via conversational interactions. Chat-crowd supports multiple agents with two conversational roles: agents who play the role of a designer are in charge of placing objects in an editable canvas according to instructions or commands issued by agents with a director role. The system can be integrated with crowdsourcing platforms for both synchronous and asynchronous data collection and is equipped with comprehensive quality controls on the performance of both types of agents. We expect that this system will be useful to build multimodal goal-oriented dialog tasks that require spatial and geometric reasoning. |
| Tasks | Goal-Oriented Dialog |
| Published | 2018-12-10 |
| URL | http://arxiv.org/abs/1812.04081v3 |
| http://arxiv.org/pdf/1812.04081v3.pdf | |
| PWC | https://paperswithcode.com/paper/chat-crowd-a-dialog-based-platform-for-visual |
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Adversarial Examples: Opportunities and Challenges
| Title | Adversarial Examples: Opportunities and Challenges |
| Authors | Jiliang Zhang, Chen Li |
| Abstract | Deep neural networks (DNNs) have shown huge superiority over humans in image recognition, speech processing, autonomous vehicles and medical diagnosis. However, recent studies indicate that DNNs are vulnerable to adversarial examples (AEs), which are designed by attackers to fool deep learning models. Different from real examples, AEs can mislead the model to predict incorrect outputs while hardly be distinguished by human eyes, therefore threaten security-critical deep-learning applications. In recent years, the generation and defense of AEs have become a research hotspot in the field of artificial intelligence (AI) security. This article reviews the latest research progress of AEs. First, we introduce the concept, cause, characteristics and evaluation metrics of AEs, then give a survey on the state-of-the-art AE generation methods with the discussion of advantages and disadvantages. After that, we review the existing defenses and discuss their limitations. Finally, future research opportunities and challenges on AEs are prospected. |
| Tasks | Autonomous Vehicles, Medical Diagnosis |
| Published | 2018-09-13 |
| URL | https://arxiv.org/abs/1809.04790v4 |
| https://arxiv.org/pdf/1809.04790v4.pdf | |
| PWC | https://paperswithcode.com/paper/adversarial-examples-opportunities-and |
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Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI
| Title | Stochastic Deep Compressive Sensing for the Reconstruction of Diffusion Tensor Cardiac MRI |
| Authors | Jo Schlemper, Guang Yang, Pedro Ferreira, Andrew Scott, Laura-Ann McGill, Zohya Khalique, Margarita Gorodezky, Malte Roehl, Jennifer Keegan, Dudley Pennell, David Firmin, Daniel Rueckert |
| Abstract | Understanding the structure of the heart at the microscopic scale of cardiomyocytes and their aggregates provides new insights into the mechanisms of heart disease and enables the investigation of effective therapeutics. Diffusion Tensor Cardiac Magnetic Resonance (DT-CMR) is a unique non-invasive technique that can resolve the microscopic structure, organisation, and integrity of the myocardium without the need for exogenous contrast agents. However, this technique suffers from relatively low signal-to-noise ratio (SNR) and frequent signal loss due to respiratory and cardiac motion. Current DT-CMR techniques rely on acquiring and averaging multiple signal acquisitions to improve the SNR. Moreover, in order to mitigate the influence of respiratory movement, patients are required to perform many breath holds which results in prolonged acquisition durations (e.g., ~30 mins using the existing technology). In this study, we propose a novel cascaded Convolutional Neural Networks (CNN) based compressive sensing (CS) technique and explore its applicability to improve DT-CMR acquisitions. Our simulation based studies have achieved high reconstruction fidelity and good agreement between DT-CMR parameters obtained with the proposed reconstruction and fully sampled ground truth. When compared to other state-of-the-art methods, our proposed deep cascaded CNN method and its stochastic variation demonstrated significant improvements. To the best of our knowledge, this is the first study using deep CNN based CS for the DT-CMR reconstruction. In addition, with relatively straightforward modifications to the acquisition scheme, our method can easily be translated into a method for online, at-the-scanner reconstruction enabling the deployment of accelerated DT-CMR in various clinical applications. |
| Tasks | Compressive Sensing |
| Published | 2018-05-30 |
| URL | http://arxiv.org/abs/1805.12064v1 |
| http://arxiv.org/pdf/1805.12064v1.pdf | |
| PWC | https://paperswithcode.com/paper/stochastic-deep-compressive-sensing-for-the |
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