April 2, 2020

3279 words 16 mins read

Paper Group ANR 372

A smile I could recognise in a thousand: Automatic identification of identity from dental radiography. The Medical Scribe: Corpus Development and Model Performance Analyses. A Survey on 3D Skeleton-Based Action Recognition Using Learning Method. Pelican: A Deep Residual Network for Network Intrusion Detection. Linguistic Fingerprints of Internet Ce …

A smile I could recognise in a thousand: Automatic identification of identity from dental radiography


Title	A smile I could recognise in a thousand: Automatic identification of identity from dental radiography
Authors	Oscar de Felice, Gustavo de Felice
Abstract	In this paper, we present a method to automatically compare multiple radiographs in order to find the identity of a patient out of the dental features. The method is based on the matching of image features, previously extracted by computer vision algorithms for image descriptor recognition. The principal application (being also our motivation to study the problem) of such a method would be in victim identification in mass disasters.
Tasks
Published	2020-01-14
URL	https://arxiv.org/abs/2001.05006v1
PDF	https://arxiv.org/pdf/2001.05006v1.pdf
PWC	https://paperswithcode.com/paper/a-smile-i-could-recognise-in-a-thousand
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The Medical Scribe: Corpus Development and Model Performance Analyses


Title	The Medical Scribe: Corpus Development and Model Performance Analyses
Authors	Izhak Shafran, Nan Du, Linh Tran, Amanda Perry, Lauren Keyes, Mark Knichel, Ashley Domin, Lei Huang, Yuhui Chen, Gang Li, Mingqiu Wang, Laurent El Shafey, Hagen Soltau, Justin S. Paul
Abstract	There is a growing interest in creating tools to assist in clinical note generation using the audio of provider-patient encounters. Motivated by this goal and with the help of providers and medical scribes, we developed an annotation scheme to extract relevant clinical concepts. We used this annotation scheme to label a corpus of about 6k clinical encounters. This was used to train a state-of-the-art tagging model. We report ontologies, labeling results, model performances, and detailed analyses of the results. Our results show that the entities related to medications can be extracted with a relatively high accuracy of 0.90 F-score, followed by symptoms at 0.72 F-score, and conditions at 0.57 F-score. In our task, we not only identify where the symptoms are mentioned but also map them to canonical forms as they appear in the clinical notes. Of the different types of errors, in about 19-38% of the cases, we find that the model output was correct, and about 17-32% of the errors do not impact the clinical note. Taken together, the models developed in this work are more useful than the F-scores reflect, making it a promising approach for practical applications.
Tasks
Published	2020-03-12
URL	https://arxiv.org/abs/2003.11531v1
PDF	https://arxiv.org/pdf/2003.11531v1.pdf
PWC	https://paperswithcode.com/paper/the-medical-scribe-corpus-development-and
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A Survey on 3D Skeleton-Based Action Recognition Using Learning Method


Title	A Survey on 3D Skeleton-Based Action Recognition Using Learning Method
Authors	Bin Ren, Mengyuan Liu, Runwei Ding, Hong Liu
Abstract	3D skeleton-based action recognition, owing to the latent advantages of skeleton, has been an active topic in computer vision. As a consequence, there are lots of impressive works including conventional handcraft feature based and learned feature based have been done over the years. However, previous surveys about action recognition mostly focus on the video or RGB data dominated methods, and the scanty existing reviews related to skeleton data mainly indicate the representation of skeleton data or performance of some classic techniques on a certain dataset. Besides, though deep learning methods has been applied to this field for years, there is no related reserach concern about an introduction or review from the perspective of deep learning architectures. To break those limitations, this survey firstly highlight the necessity of action recognition and the significance of 3D-skeleton data. Then a comprehensive introduction about Recurrent Neural Network(RNN)-based, Convolutional Neural Network(CNN)-based and Graph Convolutional Network(GCN)-based main stream action recognition techniques are illustrated in a data-driven manner. Finally, we give a brief talk about the biggest 3D skeleton dataset NTU-RGB+D and its new edition called NTU-RGB+D 120, accompanied with several existing top rank algorithms within those two datasets. To our best knowledge, this is the first research which give an overall discussion over deep learning-based action recognitin using 3D skeleton data.
Tasks	Skeleton Based Action Recognition
Published	2020-02-14
URL	https://arxiv.org/abs/2002.05907v1
PDF	https://arxiv.org/pdf/2002.05907v1.pdf
PWC	https://paperswithcode.com/paper/a-survey-on-3d-skeleton-based-action
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Pelican: A Deep Residual Network for Network Intrusion Detection


Title	Pelican: A Deep Residual Network for Network Intrusion Detection
Authors	Peilun Wu, Hui Guo
Abstract	One challenge for building a secure network communication environment is how to effectively detect and prevent malicious network behaviours. The abnormal network activities threaten users’ privacy and potentially damage the function and infrastructure of the whole network. To address this problem, the network intrusion detection system (NIDS) has been used. By continuously monitoring network activities, the system can timely identify attacks and prompt counter-attack actions. NIDS has been evolving over years. The current-generation NIDS incorporates machine learning (ML) as the core technology in order to improve the detection performance on novel attacks. However, the high detection rate achieved by a traditional ML-based detection method is often accompanied by large false-alarms, which greatly affects its overall performance. In this paper, we propose a deep neural network, Pelican, that is built upon specially-designed residual blocks. We evaluated Pelican on two network traffic datasets, NSL-KDD and UNSW-NB15. Our experiments show that Pelican can achieve a high attack detection performance while keeping a much low false alarm rate when compared with a set of up-to-date machine learning based designs.
Tasks	Intrusion Detection, Network Intrusion Detection
Published	2020-01-19
URL	https://arxiv.org/abs/2001.08523v4
PDF	https://arxiv.org/pdf/2001.08523v4.pdf
PWC	https://paperswithcode.com/paper/pelican-a-deep-residual-network-for-network
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Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo


Title	Linguistic Fingerprints of Internet Censorship: the Case of SinaWeibo
Authors	Kei Yin Ng, Anna Feldman, Jing Peng
Abstract	This paper studies how the linguistic components of blogposts collected from Sina Weibo, a Chinese microblogging platform, might affect the blogposts’ likelihood of being censored. Our results go along with King et al. (2013)‘s Collective Action Potential (CAP) theory, which states that a blogpost’s potential of causing riot or assembly in real life is the key determinant of it getting censored. Although there is not a definitive measure of this construct, the linguistic features that we identify as discriminatory go along with the CAP theory. We build a classifier that significantly outperforms non-expert humans in predicting whether a blogpost will be censored. The crowdsourcing results suggest that while humans tend to see censored blogposts as more controversial and more likely to trigger action in real life than the uncensored counterparts, they in general cannot make a better guess than our model when it comes to `reading the mind’ of the censors in deciding whether a blogpost should be censored. We do not claim that censorship is only determined by the linguistic features. There are many other factors contributing to censorship decisions. The focus of the present paper is on the linguistic form of blogposts. Our work suggests that it is possible to use linguistic properties of social media posts to automatically predict if they are going to be censored. \|
Tasks
Published	2020-01-23
URL	https://arxiv.org/abs/2001.08845v1
PDF	https://arxiv.org/pdf/2001.08845v1.pdf
PWC	https://paperswithcode.com/paper/linguistic-fingerprints-of-internet
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Framework

Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling


Title	Auto-Ensemble: An Adaptive Learning Rate Scheduling based Deep Learning Model Ensembling
Authors	Jun Yang, Fei Wang
Abstract	Ensembling deep learning models is a shortcut to promote its implementation in new scenarios, which can avoid tuning neural networks, losses and training algorithms from scratch. However, it is difficult to collect sufficient accurate and diverse models through once training. This paper proposes Auto-Ensemble (AE) to collect checkpoints of deep learning model and ensemble them automatically by adaptive learning rate scheduling algorithm. The advantage of this method is to make the model converge to various local optima by scheduling the learning rate in once training. When the number of lo-cal optimal solutions tends to be saturated, all the collected checkpoints are used for ensemble. Our method is universal, it can be applied to various scenarios. Experiment results on multiple datasets and neural networks demonstrate it is effective and competitive, especially on few-shot learning. Besides, we proposed a method to measure the distance among models. Then we can ensure the accuracy and diversity of collected models.
Tasks	Few-Shot Learning
Published	2020-03-25
URL	https://arxiv.org/abs/2003.11266v1
PDF	https://arxiv.org/pdf/2003.11266v1.pdf
PWC	https://paperswithcode.com/paper/auto-ensemble-an-adaptive-learning-rate
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Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits


Title	Adaptive Verifiability-Driven Strategy for Evolutionary Approximation of Arithmetic Circuits
Authors	Milan Ceska, Jiri Matyas, Vojtech Mrazek, Lukas Sekanina, Zdenek Vasicek, Tomas Vojnar
Abstract	We present a novel approach for designing complex approximate arithmetic circuits that trade correctness for power consumption and play important role in many energy-aware applications. Our approach integrates in a unique way formal methods providing formal guarantees on the approximation error into an evolutionary circuit optimisation algorithm. The key idea is to employ a novel adaptive search strategy that drives the evolution towards promptly verifiable approximate circuits. As demonstrated in an extensive experimental evaluation including several structurally different arithmetic circuits and target precisions, the search strategy provides superior scalability and versatility with respect to various approximation scenarios. Our approach significantly improves capabilities of the existing methods and paves a way towards an automated design process of provably-correct circuit approximations.
Tasks
Published	2020-03-05
URL	https://arxiv.org/abs/2003.02491v1
PDF	https://arxiv.org/pdf/2003.02491v1.pdf
PWC	https://paperswithcode.com/paper/adaptive-verifiability-driven-strategy-for
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EGO-TOPO: Environment Affordances from Egocentric Video


Title	EGO-TOPO: Environment Affordances from Egocentric Video
Authors	Tushar Nagarajan, Yanghao Li, Christoph Feichtenhofer, Kristen Grauman
Abstract	First-person video naturally brings the use of a physical environment to the forefront, since it shows the camera wearer interacting fluidly in a space based on his intentions. However, current methods largely separate the observed actions from the persistent space itself. We introduce a model for environment affordances that is learned directly from egocentric video. The main idea is to gain a human-centric model of a physical space (such as a kitchen) that captures (1) the primary spatial zones of interaction and (2) the likely activities they support. Our approach decomposes a space into a topological map derived from first-person activity, organizing an ego-video into a series of visits to the different zones. Further, we show how to link zones across multiple related environments (e.g., from videos of multiple kitchens) to obtain a consolidated representation of environment functionality. On EPIC-Kitchens and EGTEA+, we demonstrate our approach for learning scene affordances and anticipating future actions in long-form video.
Tasks
Published	2020-01-14
URL	https://arxiv.org/abs/2001.04583v2
PDF	https://arxiv.org/pdf/2001.04583v2.pdf
PWC	https://paperswithcode.com/paper/ego-topo-environment-affordances-from
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Odds-Ratio Thompson Sampling to Control for Time-Varying Effect


Title	Odds-Ratio Thompson Sampling to Control for Time-Varying Effect
Authors	Sulgi Kim, Kyungmin Kim
Abstract	Multi-armed bandit methods have been used for dynamic experiments particularly in online services. Among the methods, thompson sampling is widely used because it is simple but shows desirable performance. Many thompson sampling methods for binary rewards use logistic model that is written in a specific parameterization. In this study, we reparameterize logistic model with odds ratio parameters. This shows that thompson sampling can be used with subset of parameters. Based on this finding, we propose a novel method, “Odds-ratio thompson sampling”, which is expected to work robust to time-varying effect. Use of the proposed method in continuous experiment is described with discussing a desirable property of the method. In simulation studies, the novel method works robust to temporal background effect, while the loss of performance was only marginal in case with no such effect. Finally, using dataset from real service, we showed that the novel method would gain greater rewards in practical environment.
Tasks
Published	2020-03-04
URL	https://arxiv.org/abs/2003.01905v1
PDF	https://arxiv.org/pdf/2003.01905v1.pdf
PWC	https://paperswithcode.com/paper/odds-ratio-thompson-sampling-to-control-for
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Consensus-based Optimization on the Sphere II: Convergence to Global Minimizers and Machine Learning


Title	Consensus-based Optimization on the Sphere II: Convergence to Global Minimizers and Machine Learning
Authors	Massimo Fornasier, Hui Huang, Lorenzo Pareschi, Philippe Sünnen
Abstract	We present the implementation of a new stochastic Kuramoto-Vicsek-type model for global optimization of nonconvex functions on the sphere. This model belongs to the class of Consensus-Based Optimization. In fact, particles move on the sphere driven by a drift towards an instantaneous consensus point, which is computed as a convex combination of particle locations, weighted by the cost function according to Laplace’s principle, and it represents an approximation to a global minimizer. The dynamics is further perturbed by a random vector field to favor exploration, whose variance is a function of the distance of the particles to the consensus point. In particular, as soon as the consensus is reached the stochastic component vanishes. The main results of this paper are about the proof of convergence of the numerical scheme to global minimizers provided conditions of well-preparation of the initial datum. The proof combines previous results of mean-field limit with a novel asymptotic analysis, and classical convergence results of numerical methods for SDE. We present several numerical experiments, which show that the algorithm proposed in the present paper scales well with the dimension and is extremely versatile. To quantify the performances of the new approach, we show that the algorithm is able to perform essentially as good as ad hoc state of the art methods in challenging problems in signal processing and machine learning, namely the phase retrieval problem and the robust subspace detection.
Tasks
Published	2020-01-31
URL	https://arxiv.org/abs/2001.11988v3
PDF	https://arxiv.org/pdf/2001.11988v3.pdf
PWC	https://paperswithcode.com/paper/consensus-based-optimization-on-the-sphere-ii
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Policy Gradient based Quantum Approximate Optimization Algorithm


Title	Policy Gradient based Quantum Approximate Optimization Algorithm
Authors	Jiahao Yao, Marin Bukov, Lin Lin
Abstract	The quantum approximate optimization algorithm (QAOA), as a hybrid quantum/classical algorithm, has received much interest recently. QAOA can also be viewed as a variational ansatz for quantum control. However, its direct application to emergent quantum technology encounters additional physical constraints: (i) the states of the quantum system are not observable; (ii) obtaining the derivatives of the objective function can be computationally expensive or even inaccessible in experiments, and (iii) the values of the objective function may be sensitive to various sources of uncertainty, as is the case for noisy intermediate-scale quantum (NISQ) devices. Taking such constraints into account, we show that policy-gradient-based reinforcement learning (RL) algorithms are well suited for optimizing the variational parameters of QAOA in a noise-robust fashion, opening up the way for developing RL techniques for continuous quantum control. This is advantageous to help mitigate and monitor the potentially unknown sources of errors in modern quantum simulators. We analyze the performance of the algorithm for quantum state transfer problems in single- and multi-qubit systems, subject to various sources of noise such as error terms in the Hamiltonian, or quantum uncertainty in the measurement process. We show that, in noisy setups, it is capable of outperforming state-of-the-art existing optimization algorithms.
Tasks
Published	2020-02-04
URL	https://arxiv.org/abs/2002.01068v1
PDF	https://arxiv.org/pdf/2002.01068v1.pdf
PWC	https://paperswithcode.com/paper/policy-gradient-based-quantum-approximate
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Framework

Zero-Resource Cross-Domain Named Entity Recognition


Title	Zero-Resource Cross-Domain Named Entity Recognition
Authors	Zihan Liu, Genta Indra Winata, Pascale Fung
Abstract	Existing models for cross-domain named entity recognition (NER) rely on numerous unlabeled corpus or labeled NER training data in target domains. However, collecting data for low-resource target domains is not only expensive but also time-consuming. Hence, we propose a cross-domain NER model that does not use any external resources. We first introduce Multi-Task Learning (MTL) by adding a new objective function to detect whether tokens are named entities or not. We then introduce a framework called Mixture of Entity Experts (MoEE) to improve the robustness for zero-resource domain adaptation. Finally, experimental results show that our model outperforms strong unsupervised cross-domain sequence labeling models, and the performance of our model is close to that of the state-of-the-art model which leverages extensive resources.
Tasks	Cross-Domain Named Entity Recognition, Domain Adaptation, Multi-Task Learning, Named Entity Recognition
Published	2020-02-14
URL	https://arxiv.org/abs/2002.05923v1
PDF	https://arxiv.org/pdf/2002.05923v1.pdf
PWC	https://paperswithcode.com/paper/zero-resource-cross-domain-named-entity
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Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks


Title	Deep Learning for Ultra-Reliable and Low-Latency Communications in 6G Networks
Authors	Changyang She, Rui Dong, Zhouyou Gu, Zhanwei Hou, Yonghui Li, Wibowo Hardjawana, Chenyang Yang, Lingyang Song, Branka Vucetic
Abstract	In the future 6th generation networks, ultra-reliable and low-latency communications (URLLC) will lay the foundation for emerging mission-critical applications that have stringent requirements on end-to-end delay and reliability. Existing works on URLLC are mainly based on theoretical models and assumptions. The model-based solutions provide useful insights, but cannot be directly implemented in practice. In this article, we first summarize how to apply data-driven supervised deep learning and deep reinforcement learning in URLLC, and discuss some open problems of these methods. To address these open problems, we develop a multi-level architecture that enables device intelligence, edge intelligence, and cloud intelligence for URLLC. The basic idea is to merge theoretical models and real-world data in analyzing the latency and reliability and training deep neural networks (DNNs). Deep transfer learning is adopted in the architecture to fine-tune the pre-trained DNNs in non-stationary networks. Further considering that the computing capacity at each user and each mobile edge computing server is limited, federated learning is applied to improve the learning efficiency. Finally, we provide some experimental and simulation results and discuss some future directions.
Tasks	Transfer Learning
Published	2020-02-22
URL	https://arxiv.org/abs/2002.11045v1
PDF	https://arxiv.org/pdf/2002.11045v1.pdf
PWC	https://paperswithcode.com/paper/deep-learning-for-ultra-reliable-and-low
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Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes


Title	Detection and Classification of Astronomical Targets with Deep Neural Networks in Wide Field Small Aperture Telescopes
Authors	Peng Jia, Qiang Liu, Yongyang Sun
Abstract	Wide field small aperture telescopes are widely used for optical transient observations. Detection and classification of astronomical targets in observed images are the most important and basic step. In this paper, we propose an astronomical targets detection and classification framework based on deep neural networks. Our framework adopts the concept of the Faster R-CNN and uses a modified Resnet-50 as backbone network and a Feature Pyramid Network to extract features from images of different astronomical targets. To increase the generalization ability of our framework, we use both simulated and real observation images to train the neural network. After training, the neural network could detect and classify astronomical targets automatically. We test the performance of our framework with simulated data and find that our framework has almost the same detection ability as that of the traditional method for bright and isolated sources and our framework has 2 times better detection ability for dim targets, albeit all celestial objects detected by the traditional method can be classified correctly. We also use our framework to process real observation data and find that our framework can improve 25 % detection ability than that of the traditional method when the threshold of our framework is 0.6. Rapid discovery of transient targets is quite important and we further propose to install our framework in embedded devices such as the Nvidia Jetson Xavier to achieve real-time astronomical targets detection and classification abilities.
Tasks	Transfer Learning
Published	2020-02-21
URL	https://arxiv.org/abs/2002.09211v2
PDF	https://arxiv.org/pdf/2002.09211v2.pdf
PWC	https://paperswithcode.com/paper/detection-and-classification-of-astronomical
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Framework

oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions


Title	oIRL: Robust Adversarial Inverse Reinforcement Learning with Temporally Extended Actions
Authors	David Venuto, Jhelum Chakravorty, Leonard Boussioux, Junhao Wang, Gavin McCracken, Doina Precup
Abstract	Explicit engineering of reward functions for given environments has been a major hindrance to reinforcement learning methods. While Inverse Reinforcement Learning (IRL) is a solution to recover reward functions from demonstrations only, these learned rewards are generally heavily \textit{entangled} with the dynamics of the environment and therefore not portable or \emph{robust} to changing environments. Modern adversarial methods have yielded some success in reducing reward entanglement in the IRL setting. In this work, we leverage one such method, Adversarial Inverse Reinforcement Learning (AIRL), to propose an algorithm that learns hierarchical disentangled rewards with a policy over options. We show that this method has the ability to learn \emph{generalizable} policies and reward functions in complex transfer learning tasks, while yielding results in continuous control benchmarks that are comparable to those of the state-of-the-art methods.
Tasks	Continuous Control, Transfer Learning
Published	2020-02-20
URL	https://arxiv.org/abs/2002.09043v1
PDF	https://arxiv.org/pdf/2002.09043v1.pdf
PWC	https://paperswithcode.com/paper/oirl-robust-adversarial-inverse-reinforcement
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