October 17, 2019

2727 words 13 mins read

Paper Group ANR 883

Automatic Detection of Online Jihadist Hate Speech. Rethinking the Form of Latent States in Image Captioning. “With 1 follower I must be AWESOME :P”. Exploring the role of irony markers in irony recognition. Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks. Real-world plant species identification based on d …

Automatic Detection of Online Jihadist Hate Speech


Title	Automatic Detection of Online Jihadist Hate Speech
Authors	Tom De Smedt, Guy De Pauw, Pieter Van Ostaeyen
Abstract	We have developed a system that automatically detects online jihadist hate speech with over 80% accuracy, by using techniques from Natural Language Processing and Machine Learning. The system is trained on a corpus of 45,000 subversive Twitter messages collected from October 2014 to December 2016. We present a qualitative and quantitative analysis of the jihadist rhetoric in the corpus, examine the network of Twitter users, outline the technical procedure used to train the system, and discuss examples of use.
Tasks
Published	2018-03-13
URL	http://arxiv.org/abs/1803.04596v1
PDF	http://arxiv.org/pdf/1803.04596v1.pdf
PWC	https://paperswithcode.com/paper/automatic-detection-of-online-jihadist-hate
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Rethinking the Form of Latent States in Image Captioning


Title	Rethinking the Form of Latent States in Image Captioning
Authors	Bo Dai, Deming Ye, Dahua Lin
Abstract	RNNs and their variants have been widely adopted for image captioning. In RNNs, the production of a caption is driven by a sequence of latent states. Existing captioning models usually represent latent states as vectors, taking this practice for granted. We rethink this choice and study an alternative formulation, namely using two-dimensional maps to encode latent states. This is motivated by the curiosity about a question: how the spatial structures in the latent states affect the resultant captions? Our study on MSCOCO and Flickr30k leads to two significant observations. First, the formulation with 2D states is generally more effective in captioning, consistently achieving higher performance with comparable parameter sizes. Second, 2D states preserve spatial locality. Taking advantage of this, we visually reveal the internal dynamics in the process of caption generation, as well as the connections between input visual domain and output linguistic domain.
Tasks	Image Captioning
Published	2018-07-26
URL	http://arxiv.org/abs/1807.09958v1
PDF	http://arxiv.org/pdf/1807.09958v1.pdf
PWC	https://paperswithcode.com/paper/rethinking-the-form-of-latent-states-in-image
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“With 1 follower I must be AWESOME :P”. Exploring the role of irony markers in irony recognition


Title	“With 1 follower I must be AWESOME :P”. Exploring the role of irony markers in irony recognition
Authors	Debanjan Ghosh, Smaranda Muresan
Abstract	Conversations in social media often contain the use of irony or sarcasm, when the users say the opposite of what they really mean. Irony markers are the meta-communicative clues that inform the reader that an utterance is ironic. We propose a thorough analysis of theoretically grounded irony markers in two social media platforms: $Twitter$ and $Reddit$. Classification and frequency analysis show that for $Twitter$, typographic markers such as emoticons and emojis are the most discriminative markers to recognize ironic utterances, while for $Reddit$ the morphological markers (e.g., interjections, tag questions) are the most discriminative.
Tasks
Published	2018-04-14
URL	http://arxiv.org/abs/1804.05253v1
PDF	http://arxiv.org/pdf/1804.05253v1.pdf
PWC	https://paperswithcode.com/paper/with-1-follower-i-must-be-awesome-p-exploring
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Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks


Title	Wall Stress Estimation of Cerebral Aneurysm based on Zernike Convolutional Neural Networks
Authors	Zhiyu Sun, Jia Lu, Stephen Baek
Abstract	Convolutional neural networks (ConvNets) have demonstrated an exceptional capacity to discern visual patterns from digital images and signals. Unfortunately, such powerful ConvNets do not generalize well to arbitrary-shaped manifolds, where data representation does not fit into a tensor-like grid. Hence, many fields of science and engineering, where data points possess some manifold structure, cannot enjoy the full benefits of the recent advances in ConvNets. The aneurysm wall stress estimation problem introduced in this paper is one of many such problems. The problem is well-known to be of a paramount clinical importance, but yet, traditional ConvNets cannot be applied due to the manifold structure of the data, neither does the state-of-the-art geometric ConvNets perform well. Motivated by this, we propose a new geometric ConvNet method named ZerNet, which builds upon our novel mathematical generalization of convolution and pooling operations on manifolds. Our study shows that the ZerNet outperforms the other state-of-the-art geometric ConvNets in terms of accuracy.
Tasks
Published	2018-06-20
URL	http://arxiv.org/abs/1806.07441v1
PDF	http://arxiv.org/pdf/1806.07441v1.pdf
PWC	https://paperswithcode.com/paper/wall-stress-estimation-of-cerebral-aneurysm
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Real-world plant species identification based on deep convolutional neural networks and visual attention


Title	Real-world plant species identification based on deep convolutional neural networks and visual attention
Authors	Qingguo Xiao, Guangyao Li, Li Xie, Qiaochuan Chen
Abstract	This paper investigates the issue of real-world identification to fulfill better species protection. We focus on plant species identification as it is a classic and hot issue. In tradition plant species identification the samples are scanned specimen and the background is simple. However, real-world species recognition is more challenging. We first systematically investigate what is realistic species recognition and the difference from tradition plant species recognition. To deal with the challenging task, an interdisciplinary collaboration is presented based on the latest advances in computer science and technology. We propose a novel framework and an effective data augmentation method for deep learning in this paper. We first crop the image in terms of visual attention before general recognition. Besides, we apply it as a data augmentation method. We call the novel data augmentation approach attention cropping (AC). Deep convolutional neural networks are trained to predict species from a large amount of data. Extensive experiments on traditional dataset and specific dataset for real-world recognition are conducted to evaluate the performance of our approach. Experiments first demonstrate that our approach achieves state-of-the-art results on different types of datasets. Besides, we also evaluate the performance of data augmentation method AC. Results show that AC provides superior performance. Compared with the precision of methods without AC, the results with AC achieve substantial improvement.
Tasks	Data Augmentation
Published	2018-04-11
URL	http://arxiv.org/abs/1804.03853v4
PDF	http://arxiv.org/pdf/1804.03853v4.pdf
PWC	https://paperswithcode.com/paper/real-world-plant-species-identification-based
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SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection


Title	SPLAT: Semantic Pixel-Level Adaptation Transforms for Detection
Authors	Eric Tzeng, Kaylee Burns, Kate Saenko, Trevor Darrell
Abstract	Domain adaptation of visual detectors is a critical challenge, yet existing methods have overlooked pixel appearance transformations, focusing instead on bootstrapping and/or domain confusion losses. We propose a Semantic Pixel-Level Adaptation Transform (SPLAT) approach to detector adaptation that efficiently generates cross-domain image pairs. Our model uses aligned-pair and/or pseudo-label losses to adapt an object detector to the target domain, and can learn transformations with or without densely labeled data in the source (e.g. semantic segmentation annotations). Without dense labels, as is the case when only detection labels are available in the source, transformations are learned using CycleGAN alignment. Otherwise, when dense labels are available we introduce a more efficient cycle-free method, which exploits pixel-level semantic labels to condition the training of the transformation network. The end task is then trained using detection box labels from the source, potentially including labels inferred on unlabeled source data. We show both that pixel-level transforms outperform prior approaches to detector domain adaptation, and that our cycle-free method outperforms prior models for unconstrained cycle-based learning of generic transformations while running 3.8 times faster. Our combined model improves on prior detection baselines by 12.5 mAP adapting from Sim 10K to Cityscapes, recovering over 50% of the missing performance between the unadapted baseline and the labeled-target upper bound.
Tasks	Domain Adaptation, Semantic Segmentation
Published	2018-12-03
URL	http://arxiv.org/abs/1812.00929v1
PDF	http://arxiv.org/pdf/1812.00929v1.pdf
PWC	https://paperswithcode.com/paper/splat-semantic-pixel-level-adaptation
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Global Optimality in Distributed Low-rank Matrix Factorization


Title	Global Optimality in Distributed Low-rank Matrix Factorization
Authors	Zhihui Zhu, Qiuwei Li, Xinshuo Yang, Gongguo Tang, Michael B. Wakin
Abstract	We study the convergence of a variant of distributed gradient descent (DGD) on a distributed low-rank matrix approximation problem wherein some optimization variables are used for consensus (as in classical DGD) and some optimization variables appear only locally at a single node in the network. We term the resulting algorithm DGD+LOCAL. Using algorithmic connections to gradient descent and geometric connections to the well-behaved landscape of the centralized low-rank matrix approximation problem, we identify sufficient conditions where DGD+LOCAL is guaranteed to converge with exact consensus to a global minimizer of the original centralized problem. For the distributed low-rank matrix approximation problem, these guarantees are stronger—in terms of consensus and optimality—than what appear in the literature for classical DGD and more general problems.
Tasks
Published	2018-11-07
URL	http://arxiv.org/abs/1811.03129v2
PDF	http://arxiv.org/pdf/1811.03129v2.pdf
PWC	https://paperswithcode.com/paper/global-optimality-in-distributed-low-rank
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Multi-Kernel Regression with Sparsity Constraint


Title	Multi-Kernel Regression with Sparsity Constraint
Authors	Shayan Aziznejad, Michael Unser
Abstract	In this paper, we provide a Banach-space formulation of supervised learning with generalized total-variation (gTV) regularization. We identify the class of kernel functions that are admissible in this framework. Then, we propose a variation of supervised learning in a continuous-domain hybrid search space with gTV regularization. We show that the solution admits a multi-kernel expansion with adaptive positions. In this representation, the number of active kernels is upper-bounded by the number of data points while the gTV regularization imposes an $\ell_1$ penalty on the kernel coefficients. Finally, we illustrate numerically the outcome of our theory.
Tasks
Published	2018-11-02
URL	https://arxiv.org/abs/1811.00836v3
PDF	https://arxiv.org/pdf/1811.00836v3.pdf
PWC	https://paperswithcode.com/paper/an-l1-representer-theorem-for-multiple-kernel
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Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models


Title	Deep Variational Transfer: Transfer Learning through Semi-supervised Deep Generative Models
Authors	Marouan Belhaj, Pavlos Protopapas, Weiwei Pan
Abstract	In real-world applications, it is often expensive and time-consuming to obtain labeled examples. In such cases, knowledge transfer from related domains, where labels are abundant, could greatly reduce the need for extensive labeling efforts. In this scenario, transfer learning comes in hand. In this paper, we propose Deep Variational Transfer (DVT), a variational autoencoder that transfers knowledge across domains using a shared latent Gaussian mixture model. Thanks to the combination of a semi-supervised ELBO and parameters sharing across domains, we are able to simultaneously: (i) align all supervised examples of the same class into the same latent Gaussian Mixture component, independently from their domain; (ii) predict the class of unsupervised examples from different domains and use them to better model the occurring shifts. We perform tests on MNIST and USPS digits datasets, showing DVT’s ability to perform transfer learning across heterogeneous datasets. Additionally, we present DVT’s top classification performances on the MNIST semi-supervised learning challenge. We further validate DVT on a astronomical datasets. DVT achieves states-of-the-art classification performances, transferring knowledge across real stars surveys datasets, EROS, MACHO and HiTS, . In the worst performance, we double the achieved F1-score for rare classes. These experiments show DVT’s ability to tackle all major challenges posed by transfer learning: different covariate distributions, different and highly imbalanced class distributions and different feature spaces.
Tasks	Transfer Learning
Published	2018-12-07
URL	http://arxiv.org/abs/1812.03123v1
PDF	http://arxiv.org/pdf/1812.03123v1.pdf
PWC	https://paperswithcode.com/paper/deep-variational-transfer-transfer-learning
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Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols


Title	Leveraging Textual Specifications for Grammar-based Fuzzing of Network Protocols
Authors	Samuel Jero, Maria Leonor Pacheco, Dan Goldwasser, Cristina Nita-Rotaru
Abstract	Grammar-based fuzzing is a technique used to find software vulnerabilities by injecting well-formed inputs generated following rules that encode application semantics. Most grammar-based fuzzers for network protocols rely on human experts to manually specify these rules. In this work we study automated learning of protocol rules from textual specifications (i.e. RFCs). We evaluate the automatically extracted protocol rules by applying them to a state-of-the-art fuzzer for transport protocols and show that it leads to a smaller number of test cases while finding the same attacks as the system that uses manually specified rules.
Tasks
Published	2018-10-10
URL	http://arxiv.org/abs/1810.04755v1
PDF	http://arxiv.org/pdf/1810.04755v1.pdf
PWC	https://paperswithcode.com/paper/leveraging-textual-specifications-for-grammar
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Framework

Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection


Title	Self-supervised Learning of Geometrically Stable Features Through Probabilistic Introspection
Authors	David Novotny, Samuel Albanie, Diane Larlus, Andrea Vedaldi
Abstract	Self-supervision can dramatically cut back the amount of manually-labelled data required to train deep neural networks. While self-supervision has usually been considered for tasks such as image classification, in this paper we aim at extending it to geometry-oriented tasks such as semantic matching and part detection. We do so by building on several recent ideas in unsupervised landmark detection. Our approach learns dense distinctive visual descriptors from an unlabelled dataset of images using synthetic image transformations. It does so by means of a robust probabilistic formulation that can introspectively determine which image regions are likely to result in stable image matching. We show empirically that a network pre-trained in this manner requires significantly less supervision to learn semantic object parts compared to numerous pre-training alternatives. We also show that the pre-trained representation is excellent for semantic object matching.
Tasks	Image Classification
Published	2018-04-04
URL	http://arxiv.org/abs/1804.01552v1
PDF	http://arxiv.org/pdf/1804.01552v1.pdf
PWC	https://paperswithcode.com/paper/self-supervised-learning-of-geometrically
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Framework

End-to-end Learning of Multi-sensor 3D Tracking by Detection


Title	End-to-end Learning of Multi-sensor 3D Tracking by Detection
Authors	Davi Frossard, Raquel Urtasun
Abstract	In this paper we propose a novel approach to tracking by detection that can exploit both cameras as well as LIDAR data to produce very accurate 3D trajectories. Towards this goal, we formulate the problem as a linear program that can be solved exactly, and learn convolutional networks for detection as well as matching in an end-to-end manner. We evaluate our model in the challenging KITTI dataset and show very competitive results.
Tasks
Published	2018-06-29
URL	http://arxiv.org/abs/1806.11534v1
PDF	http://arxiv.org/pdf/1806.11534v1.pdf
PWC	https://paperswithcode.com/paper/end-to-end-learning-of-multi-sensor-3d
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Framework

Adversarial TableQA: Attention Supervision for Question Answering on Tables


Title	Adversarial TableQA: Attention Supervision for Question Answering on Tables
Authors	Minseok Cho, Reinald Kim Amplayo, Seung-won Hwang, Jonghyuck Park
Abstract	The task of answering a question given a text passage has shown great developments on model performance thanks to community efforts in building useful datasets. Recently, there have been doubts whether such rapid progress has been based on truly understanding language. The same question has not been asked in the table question answering (TableQA) task, where we are tasked to answer a query given a table. We show that existing efforts, of using “answers” for both evaluation and supervision for TableQA, show deteriorating performances in adversarial settings of perturbations that do not affect the answer. This insight naturally motivates to develop new models that understand question and table more precisely. For this goal, we propose Neural Operator (NeOp), a multi-layer sequential network with attention supervision to answer the query given a table. NeOp uses multiple Selective Recurrent Units (SelRUs) to further help the interpretability of the answers of the model. Experiments show that the use of operand information to train the model significantly improves the performance and interpretability of TableQA models. NeOp outperforms all the previous models by a big margin.
Tasks	Question Answering
Published	2018-10-18
URL	http://arxiv.org/abs/1810.08113v2
PDF	http://arxiv.org/pdf/1810.08113v2.pdf
PWC	https://paperswithcode.com/paper/adversarial-tableqa-attention-supervision-for
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Urban-Rural Environmental Gradient in a Developing City: Testing ENVI GIS Functionality


Title	Urban-Rural Environmental Gradient in a Developing City: Testing ENVI GIS Functionality
Authors	Polina Lemenkova
Abstract	The research performs urban ecosystem analysis supported by ENVI GIS by integrated studies on land cover types and geospatial modeling of Taipei city. The paper deals with the role of anthropogenic pressure on the structure of the landscape and change of land cover types. Methods included assessment of the impact from anthropogenic activities on the natural ecosystems, evaluation of the rate and scale of landscape dynamics using remote sensing data and GIS. The research aims to assist environmentalists and city planners to evaluate strategies for specific objectives of urban development in Taiwan, China.
Tasks
Published	2018-12-06
URL	http://arxiv.org/abs/1812.10378v1
PDF	http://arxiv.org/pdf/1812.10378v1.pdf
PWC	https://paperswithcode.com/paper/urban-rural-environmental-gradient-in-a
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Framework

The Lower The Simpler: Simplifying Hierarchical Recurrent Models


Title	The Lower The Simpler: Simplifying Hierarchical Recurrent Models
Authors	Chao Wang, Hui Jiang
Abstract	To improve the training efficiency of hierarchical recurrent models without compromising their performance, we propose a strategy named as `the lower the simpler’, which is to simplify the baseline models by making the lower layers simpler than the upper layers. We carry out this strategy to simplify two typical hierarchical recurrent models, namely Hierarchical Recurrent Encoder-Decoder (HRED) and R-NET, whose basic building block is GRU. Specifically, we propose Scalar Gated Unit (SGU), which is a simplified variant of GRU, and use it to replace the GRUs at the middle layers of HRED and R-NET. Besides, we also use Fixed-size Ordinally-Forgetting Encoding (FOFE), which is an efficient encoding method without any trainable parameter, to replace the GRUs at the bottom layers of HRED and R-NET. The experimental results show that the simplified HRED and the simplified R-NET contain significantly less trainable parameters, consume significantly less training time, and achieve slightly better performance than their baseline models. \|
Tasks
Published	2018-09-08
URL	https://arxiv.org/abs/1809.02790v4
PDF	https://arxiv.org/pdf/1809.02790v4.pdf
PWC	https://paperswithcode.com/paper/simplified-hierarchical-recurrent-encoder
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