January 26, 2020

3082 words 15 mins read

Paper Group ANR 1482

A CNN toolbox for skin cancer classification. Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data. Spectral Characterization of functional MRI data on voxel-resolution cortical graphs. Control-Tutored Reinforcement Learning: an application to the Herding Problem. Neural Check-Worthiness Ranking with Weak Supervisi …

A CNN toolbox for skin cancer classification


Title	A CNN toolbox for skin cancer classification
Authors	Fabrizio Nunnari, Daniel Sonntag
Abstract	We describe a software toolbox for the configuration of deep neural networks in the domain of skin cancer classification. The implemented software architecture allows developers to quickly set up new convolutional neural network (CNN) architectures and hyper-parameter configurations. At the same time, the user interface, manageable as a simple spreadsheet, allows non-technical users to explore different configuration settings that need to be explored when switching to different data sets. In future versions, meta leaning frameworks can be added, or AutoML systems that continuously improve over time. Preliminary results, conducted with two CNNs in the context melanoma detection on dermoscopic images, quantify the impact of image augmentation, image resolution, and rescaling filter on the overall detection performance and training time.
Tasks	AutoML, Image Augmentation, Skin Cancer Classification
Published	2019-08-21
URL	https://arxiv.org/abs/1908.08187v1
PDF	https://arxiv.org/pdf/1908.08187v1.pdf
PWC	https://paperswithcode.com/paper/a-cnn-toolbox-for-skin-cancer-classification
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Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data


Title	Endotracheal Tube Detection and Segmentation in Chest Radiographs using Synthetic Data
Authors	Maayan Frid-Adar, Rula Amer, Hayit Greenspan
Abstract	Chest radiographs are frequently used to verify the correct intubation of patients in the emergency room. Fast and accurate identification and localization of the endotracheal (ET) tube is critical for the patient. In this study we propose a novel automated deep learning scheme for accurate detection and segmentation of the ET tubes. Development of automatic systems using deep learning networks for classification and segmentation require large annotated data which is not always available. Here we present an approach for synthesizing ET tubes in real X-ray images. We suggest a method for training the network, first with synthetic data and then with real X-ray images in a fine-tuning phase, which allows the network to train on thousands of cases without annotating any data. The proposed method was tested on 477 real chest radiographs from a public dataset and reached AUC of 0.99 in classifying the presence vs. absence of the ET tube, along with outputting high quality ET tube segmentation maps.
Tasks
Published	2019-08-20
URL	https://arxiv.org/abs/1908.07170v1
PDF	https://arxiv.org/pdf/1908.07170v1.pdf
PWC	https://paperswithcode.com/paper/endotracheal-tube-detection-and-segmentation
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Spectral Characterization of functional MRI data on voxel-resolution cortical graphs


Title	Spectral Characterization of functional MRI data on voxel-resolution cortical graphs
Authors	Hamid Behjat, Martin Larsson
Abstract	The human cortical layer exhibits a convoluted morphology that is unique to each individual. Conventional volumetric fMRI processing schemes take for granted the rich information provided by the underlying anatomy. We present a method to study fMRI data on subject-specific cerebral hemisphere cortex (CHC) graphs, which encode the cortical morphology at the resolution of voxels in 3-D. We study graph spectral energy metrics associated to fMRI data of 100 subjects from the Human Connectome Project database, across seven tasks. Experimental results signify the strength of CHC graphs’ Laplacian eigenvector bases in capturing subtle spatial patterns specific to different functional loads as well as experimental conditions within each task.
Tasks
Published	2019-10-21
URL	https://arxiv.org/abs/1910.09507v2
PDF	https://arxiv.org/pdf/1910.09507v2.pdf
PWC	https://paperswithcode.com/paper/spectral-characterization-of-functional-mri
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Control-Tutored Reinforcement Learning: an application to the Herding Problem


Title	Control-Tutored Reinforcement Learning: an application to the Herding Problem
Authors	Francesco De Lellis, Fabrizia Auletta, Giovanni Russo, Mario di Bernardo
Abstract	In this extended abstract we introduce a novel control-tutored Q-learning approach (CTQL) as part of the ongoing effort in developing model-based and safe RL for continuous state spaces. We validate our approach by applying it to a challenging multi-agent herding control problem.
Tasks	Q-Learning
Published	2019-11-26
URL	https://arxiv.org/abs/1911.11444v2
PDF	https://arxiv.org/pdf/1911.11444v2.pdf
PWC	https://paperswithcode.com/paper/control-tutored-reinforcement-learning-an
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Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking


Title	Neural Check-Worthiness Ranking with Weak Supervision: Finding Sentences for Fact-Checking
Authors	Casper Hansen, Christian Hansen, Stephen Alstrup, Jakob Grue Simonsen, Christina Lioma
Abstract	Automatic fact-checking systems detect misinformation, such as fake news, by (i) selecting check-worthy sentences for fact-checking, (ii) gathering related information to the sentences, and (iii) inferring the factuality of the sentences. Most prior research on (i) uses hand-crafted features to select check-worthy sentences, and does not explicitly account for the recent finding that the top weighted terms in both check-worthy and non-check-worthy sentences are actually overlapping [15]. Motivated by this, we present a neural check-worthiness sentence ranking model that represents each word in a sentence by \textit{both} its embedding (aiming to capture its semantics) and its syntactic dependencies (aiming to capture its role in modifying the semantics of other terms in the sentence). Our model is an end-to-end trainable neural network for check-worthiness ranking, which is trained on large amounts of unlabelled data through weak supervision. Thorough experimental evaluation against state of the art baselines, with and without weak supervision, shows our model to be superior at all times (+13% in MAP and +28% at various Precision cut-offs from the best baseline with statistical significance). Empirical analysis of the use of weak supervision, word embedding pretraining on domain-specific data, and the use of syntactic dependencies of our model reveals that check-worthy sentences contain notably more identical syntactic dependencies than non-check-worthy sentences.
Tasks
Published	2019-03-20
URL	http://arxiv.org/abs/1903.08404v1
PDF	http://arxiv.org/pdf/1903.08404v1.pdf
PWC	https://paperswithcode.com/paper/neural-check-worthiness-ranking-with-weak
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TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs


Title	TEAGS: Time-aware Text Embedding Approach to Generate Subgraphs
Authors	Saeid Hosseini, Saeed Najafipour, Ngai-Man Cheung, Hongzhi Yin, Mohammad Reza Kangavari, Xiaofang Zhou
Abstract	Contagions (e.g. virus, gossip) spread over the nodes in propagation graphs. We can use the temporal and textual data of the nodes to compute the edge weights and then generate subgraphs with highly relevant nodes. This is beneficial to many applications. Yet, challenges abound. First, the propagation pattern between each pair of nodes may change by time. Second, not always the same contagion propagates. Hence, the state-of-the-art text mining approaches including topic-modeling cannot effectively compute the edge weights. Third, since the propagation is affected by time, the word-word co-occurrence patterns may differ in various temporal dimensions, that can decrease the effectiveness of word embedding approaches. We argue that multi-aspect temporal dimensions (hour, day, etc) should be considered to better calculate the correlation weights between the nodes. In this work, we devise a novel framework that on the one hand, integrates a neural network based time-aware word embedding component to construct the word vectors through multiple temporal facets, and on the other hand, uses a temporal generative model to compute the weights. Subsequently, we propose a Max-Heap Graph cutting algorithm to generate subgraphs. We validate our model through comprehensive experiments on real-world datasets. The results show that our model can retrieve the subgraphs more effective than other rivals and the temporal dynamics should be noticed both in word embedding and propagation processes.
Tasks
Published	2019-07-06
URL	https://arxiv.org/abs/1907.03191v3
PDF	https://arxiv.org/pdf/1907.03191v3.pdf
PWC	https://paperswithcode.com/paper/teals-time-aware-text-embedding-approach-to
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Tracking Behavioral Patterns among Students in an Online Educational System


Title	Tracking Behavioral Patterns among Students in an Online Educational System
Authors	Stephan Lorenzen, Niklas Hjuler, Stephen Alstrup
Abstract	Analysis of log data generated by online educational systems is an essential task to better the educational systems and increase our understanding of how students learn. In this study we investigate previously unseen data from Clio Online, the largest provider of digital learning content for primary schools in Denmark. We consider data for 14,810 students with 3 million sessions in the period 2015-2017. We analyze student activity in periods of one week. By using non-negative matrix factorization techniques, we obtain soft clusterings, revealing dependencies among time of day, subject, activity type, activity complexity (measured by Bloom’s taxonomy), and performance. Furthermore, our method allows for tracking behavioral changes of individual students over time, as well as general behavioral changes in the educational system. Based on the results, we give suggestions for behavioral changes, in order to optimize the learning experience and improve performance.
Tasks
Published	2019-08-21
URL	https://arxiv.org/abs/1908.08937v1
PDF	https://arxiv.org/pdf/1908.08937v1.pdf
PWC	https://paperswithcode.com/paper/tracking-behavioral-patterns-among-students
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Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation


Title	Judge the Judges: A Large-Scale Evaluation Study of Neural Language Models for Online Review Generation
Authors	Cristina Garbacea, Samuel Carton, Shiyan Yan, Qiaozhu Mei
Abstract	We conduct a large-scale, systematic study to evaluate the existing evaluation methods for natural language generation in the context of generating online product reviews. We compare human-based evaluators with a variety of automated evaluation procedures, including discriminative evaluators that measure how well machine-generated text can be distinguished from human-written text, as well as word overlap metrics that assess how similar the generated text compares to human-written references. We determine to what extent these different evaluators agree on the ranking of a dozen of state-of-the-art generators for online product reviews. We find that human evaluators do not correlate well with discriminative evaluators, leaving a bigger question of whether adversarial accuracy is the correct objective for natural language generation. In general, distinguishing machine-generated text is challenging even for human evaluators, and human decisions correlate better with lexical overlaps. We find lexical diversity an intriguing metric that is indicative of the assessments of different evaluators. A post-experiment survey of participants provides insights into how to evaluate and improve the quality of natural language generation systems.
Tasks	Text Generation
Published	2019-01-02
URL	https://arxiv.org/abs/1901.00398v2
PDF	https://arxiv.org/pdf/1901.00398v2.pdf
PWC	https://paperswithcode.com/paper/judge-the-judges-a-large-scale-evaluation
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Imperceptible Adversarial Attacks on Tabular Data


Title	Imperceptible Adversarial Attacks on Tabular Data
Authors	Vincent Ballet, Xavier Renard, Jonathan Aigrain, Thibault Laugel, Pascal Frossard, Marcin Detyniecki
Abstract	Security of machine learning models is a concern as they may face adversarial attacks for unwarranted advantageous decisions. While research on the topic has mainly been focusing on the image domain, numerous industrial applications, in particular in finance, rely on standard tabular data. In this paper, we discuss the notion of adversarial examples in the tabular domain. We propose a formalization based on the imperceptibility of attacks in the tabular domain leading to an approach to generate imperceptible adversarial examples. Experiments show that we can generate imperceptible adversarial examples with a high fooling rate.
Tasks
Published	2019-11-08
URL	https://arxiv.org/abs/1911.03274v2
PDF	https://arxiv.org/pdf/1911.03274v2.pdf
PWC	https://paperswithcode.com/paper/imperceptible-adversarial-attacks-on-tabular
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Feature Fusion Detector for Semantic Cognition of Remote Sensing


Title	Feature Fusion Detector for Semantic Cognition of Remote Sensing
Authors	Wei Zhou, Yiying Li
Abstract	The value of remote sensing images is of vital importance in many areas and needs to be refined by some cognitive approaches. The remote sensing detection is an appropriate way to achieve the semantic cognition. However, such detection is a challenging issue for scale diversity, diversity of views, small objects, sophisticated light and shadow backgrounds. In this article, inspired by the state-of-the-art detection framework FPN, we propose a novel approach for constructing a feature fusion module that optimizes feature context utilization in detection, calling our system LFFN for Layer-weakening Feature Fusion Network. We explore the inherent relevance of different layers to the final decision, and the incentives of higher-level features to lower-level features. More importantly, we explore the characteristics of different backbone networks in the mining of basic features and the correlation utilization of convolutional channels, and call our upgraded version as advanced LFFN. Based on experiments on the remote sensing dataset from Google Earth, our LFFN has proved effective and practical for the semantic cognition of remote sensing, achieving 89% mAP which is 4.1% higher than that of FPN. Moreover, in terms of the generalization performance, LFFN achieves 79.9% mAP on VOC 2007 and achieves 73.0% mAP on VOC 2012 test, and advacned LFFN obtains the mAP values of 80.7% and 74.4% on VOC 2007 and 2012 respectively, outperforming the comparable state-of-the-art SSD and Faster R-CNN models.
Tasks
Published	2019-09-28
URL	https://arxiv.org/abs/1909.13047v1
PDF	https://arxiv.org/pdf/1909.13047v1.pdf
PWC	https://paperswithcode.com/paper/feature-fusion-detector-for-semantic
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Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning


Title	Mitigate Bias in Face Recognition using Skewness-Aware Reinforcement Learning
Authors	Mei Wang, Weihong Deng
Abstract	Racial equality is an important theme of international human rights law, but it has been largely obscured when the overall face recognition accuracy is pursued blindly. More facts indicate racial bias indeed degrades the fairness of recognition system and the error rates on non-Caucasians are usually much higher than Caucasians. To encourage fairness, we introduce the idea of adaptive margin to learn balanced performance for different races based on large margin losses. A reinforcement learning based race balance network (RL-RBN) is proposed. We formulate the process of finding the optimal margins for non-Caucasians as a Markov decision process and employ deep Q-learning to learn policies for an agent to select appropriate margin by approximating the Q-value function. Guided by the agent, the skewness of feature scatter between races can be reduced. Besides, we provide two ethnicity aware training datasets, called BUPT-Globalface and BUPT-Balancedface dataset, which can be utilized to study racial bias from both data and algorithm aspects. Extensive experiments on RFW database show that RL-RBN successfully mitigates racial bias and learns more balanced performance for different races.
Tasks	Face Recognition, Q-Learning
Published	2019-11-25
URL	https://arxiv.org/abs/1911.10692v1
PDF	https://arxiv.org/pdf/1911.10692v1.pdf
PWC	https://paperswithcode.com/paper/mitigate-bias-in-face-recognition-using
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Land Use and Land Cover Classification Using Deep Learning Techniques


Title	Land Use and Land Cover Classification Using Deep Learning Techniques
Authors	Nagesh Kumar Uba
Abstract	Large datasets of sub-meter aerial imagery represented as orthophoto mosaics are widely available today, and these data sets may hold a great deal of untapped information. This imagery has a potential to locate several types of features; for example, forests, parking lots, airports, residential areas, or freeways in the imagery. However, the appearances of these things vary based on many things including the time that the image is captured, the sensor settings, processing done to rectify the image, and the geographical and cultural context of the region captured by the image. This thesis explores the use of deep convolutional neural networks to classify land use from very high spatial resolution (VHR), orthorectified, visible band multispectral imagery. Recent technological and commercial applications have driven the collection a massive amount of VHR images in the visible red, green, blue (RGB) spectral bands, this work explores the potential for deep learning algorithms to exploit this imagery for automatic land use/ land cover (LULC) classification.
Tasks
Published	2019-05-01
URL	http://arxiv.org/abs/1905.00510v1
PDF	http://arxiv.org/pdf/1905.00510v1.pdf
PWC	https://paperswithcode.com/paper/land-use-and-land-cover-classification-using
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Alleviating Privacy Attacks via Causal Learning


Title	Alleviating Privacy Attacks via Causal Learning
Authors	Shruti Tople, Amit Sharma, Aditya Nori
Abstract	Machine learning models, especially deep neural networks have been shown to reveal membership information of inputs in the training data. Such membership inference attacks are a serious privacy concern, for example, patients providing medical records to build a model that detects HIV would not want their identity to be leaked. Further, we show that the attack accuracy amplifies when the model is used to predict samples that come from a different distribution than the training set, which is often the case in real world applications. Therefore, we propose the use of causal learning approaches where a model learns the causal relationship between the input features and the outcome. An ideal causal model is known to be invariant to the training distribution and hence generalizes well to shifts between samples from the same distribution and across different distributions. First, we prove that models learned using causal structure provide stronger differential privacy guarantees than associational models under reasonable assumptions. Next, we show that causal models trained on sufficiently large samples are robust to membership inference attacks across different distributions of datasets and those trained on smaller sample sizes always have lower attack accuracy than corresponding associational models. Finally, we confirm our theoretical claims with experimental evaluation on 4 moderately complex Bayesian network datasets and a colored MNIST image dataset. Associational models exhibit upto 80% attack accuracy under different test distributions and sample sizes whereas causal models exhibit attack accuracy close to a random guess. Our results confirm the value of the generalizability of causal models in reducing susceptibility to privacy attacks.
Tasks
Published	2019-09-27
URL	https://arxiv.org/abs/1909.12732v2
PDF	https://arxiv.org/pdf/1909.12732v2.pdf
PWC	https://paperswithcode.com/paper/alleviating-privacy-attacks-via-causal-1
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Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations


Title	Self-Weighted Multiview Metric Learning by Maximizing the Cross Correlations
Authors	Huibing Wang, Jinjia Peng, Xianping Fu
Abstract	With the development of multimedia time, one sample can always be described from multiple views which contain compatible and complementary information. Most algorithms cannot take information from multiple views into considerations and fail to achieve desirable performance in most situations. For many applications, such as image retrieval, face recognition, etc., an appropriate distance metric can better reflect the similarities between various samples. Therefore, how to construct a good distance metric learning methods which can deal with multiview data has been an important topic during the last decade. In this paper, we proposed a novel algorithm named Self-weighted Multiview Metric Learning (SM2L) which can finish this task by maximizing the cross correlations between different views. Furthermore, because multiple views have different contributions to the learning procedure of SM2L, we adopt a self-weighted learning framework to assign multiple views with different weights. Various experiments on benchmark datasets can verify the performance of our proposed method.
Tasks	Face Recognition, Image Retrieval, Metric Learning
Published	2019-03-19
URL	http://arxiv.org/abs/1903.07812v1
PDF	http://arxiv.org/pdf/1903.07812v1.pdf
PWC	https://paperswithcode.com/paper/self-weighted-multiview-metric-learning-by
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Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview


Title	Predictive Biases in Natural Language Processing Models: A Conceptual Framework and Overview
Authors	Deven Shah, H. Andrew Schwartz, Dirk Hovy
Abstract	An increasing number of works in natural language processing have addressed the effect of bias on the predicted outcomes, introducing mitigation techniques that act on different parts of the standard NLP pipeline (data and models). However, these works have been conducted in isolation, without a unifying framework to organize efforts within the field. This leads to repetitive approaches, and puts an undue focus on the effects of bias, rather than on their origins. Research focused on bias symptoms rather than the underlying origins could limit the development of effective countermeasures. In this paper, we propose a unifying conceptualization: the predictive bias framework for NLP. We summarize the NLP literature and propose a general mathematical definition of predictive bias in NLP along with a conceptual framework, differentiating four main origins of biases: label bias, selection bias, model overamplification, and semantic bias. We discuss how past work has countered each bias origin. Our framework serves to guide an introductory overview of predictive bias in NLP, integrating existing work into a single structure and opening avenues for future research.
Tasks
Published	2019-11-09
URL	https://arxiv.org/abs/1912.11078v1
PDF	https://arxiv.org/pdf/1912.11078v1.pdf
PWC	https://paperswithcode.com/paper/predictive-biases-in-natural-language
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