2020.05.08.txt

==========New Papers==========
1, TITLE:       Quda: Natural Language Queries for Visual Data Analytics
http://arxiv.org/abs/2005.03257
AUTHORS:        Siwei Fu ; Kai Xiong ; Xiaodong Ge ; Yingcai Wu ; Siliang Tang ; Wei Chen
HIGHLIGHT:      We present a new dataset, called Quda, to help V-NLIs understand free-form natural language.

2, TITLE:       Critique of Boyu Sima's Proof that ${\rm P}\neq{\rm NP}$
http://arxiv.org/abs/2005.03256
AUTHORS:        Brendon Pon
COMMENTS:       7 pages
HIGHLIGHT:      We review and critique Boyu Sima's paper, "A solution of the P versus NP problem based on specific property of clique function," (arXiv:1911.00722) which claims to prove that ${\rm P}\neq{\rm NP}$ by way of removing the gap between the nonmonotone circuit complexity and the monotone circuit complexity of the clique function.

3, TITLE:       Text Recognition in the Wild: A Survey
http://arxiv.org/abs/2005.03492
AUTHORS:        Xiaoxue Chen ; Lianwen Jin ; Yuanzhi Zhu ; Canjie Luo ; Tianwei Wang
HIGHLIGHT:      This paper aims to (1) summarize the fundamental problems and the state-of-the-art associated with scene text recognition; (2) introduce new insights and ideas; (3) provide a comprehensive review of publicly available resources; (4) point out directions for future work.

4, TITLE:       Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT
http://arxiv.org/abs/2005.03264
AUTHORS:        Liang Sun ; Zhanhao Mo ; Fuhua Yan ; Liming Xia ; Fei Shan ; Zhongxiang Ding ; Wei Shao ; Feng Shi ; Huan Yuan ; Huiting Jiang ; Dijia Wu ; Ying Wei ; Yaozong Gao ; Wanchun Gao ; He Sui ; Daoqiang Zhang ; Dinggang Shen
HIGHLIGHT:      In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images.

5, TITLE:       Extracting Headless MWEs from Dependency Parse Trees: Parsing, Tagging, and Joint Modeling Approaches
http://arxiv.org/abs/2005.03035
AUTHORS:        Tianze Shi ; Lillian Lee
COMMENTS:       Proceedings of ACL, 2020
HIGHLIGHT:      We empirically compare these two common strategies--parsing and tagging--for predicting flat MWEs.

6, TITLE:       RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
http://arxiv.org/abs/2005.03271
AUTHORS:        Chung-Cheng Chiu ; Arun Narayanan ; Wei Han ; Rohit Prabhavalkar ; Yu Zhang ; Navdeep Jaitly ; Ruoming Pang ; Tara N. Sainath ; Patrick Nguyen ; Liangliang Cao ; Yonghui Wu
HIGHLIGHT:      We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference.

7, TITLE:       Multi-view data capture using edge-synchronised mobiles
http://arxiv.org/abs/2005.03286
AUTHORS:        Matteo Bortolon ; Paul Chippendale ; Stefano Messelodi ; Fabio Poiesi
HIGHLIGHT:      We propose a novel and scalable data capture architecture that exploits edge resources to synchronise and harvest frame captures.

8, TITLE:       CovidCTNet: An Open-Source Deep Learning Approach to Identify Covid-19 Using CT Image
http://arxiv.org/abs/2005.03059
AUTHORS:        Tahereh Javaheri ; Morteza Homayounfar ; Zohreh Amoozgar ; Reza Reiazi ; Fatemeh Homayounieh ; Engy Abbas ; Azadeh Laali ; Amir Reza Radmard ; Mohammad Hadi Gharib ; Seyed Ali Javad Mousavi ; Omid Ghaemi ; Rosa Babaei ; Hadi Karimi Mobin ; Mehdi Hosseinzadeh ; Rana Jahanban-Esfahlan ; Khaled Seidi ; Mannudeep K. Kalra ; Guanglan Zhang ; L. T. Chitkushev ; Benjamin Haibe-Kains ; Reza Malekzadeh ; Reza Rawassizadeh
COMMENTS:       5 figures
HIGHLIGHT:      To enhance the accuracy of CT imaging detection, we developed an open-source set of algorithms called CovidCTNet that successfully differentiates Covid-19 from community-acquired pneumonia (CAP) and other lung diseases.

9, TITLE:       Adaptive Dialog Policy Learning with Hindsight and User Modeling
http://arxiv.org/abs/2005.03299
AUTHORS:        Yan Cao ; Keting Lu ; Xiaoping Chen ; Shiqi Zhang
HIGHLIGHT:      Reinforcement learning methods have been used to compute dialog policies from language-based interaction experiences.

10, TITLE:      Knowledge Enhanced Neural Fashion Trend Forecasting
http://arxiv.org/abs/2005.03297
AUTHORS:        Yunshan Ma ; Yujuan Ding ; Xun Yang ; Lizi Liao ; Wai Keung Wong ; Tat-Seng Chua
COMMENTS:       8 pages, 9 figures, ICMR 2020
HIGHLIGHT:      Towards insightful fashion trend forecasting, this work focuses on investigating fine-grained fashion element trends for specific user groups.

11, TITLE:      Deep Learning based Person Re-identification
http://arxiv.org/abs/2005.03293
AUTHORS:        Nirbhay Kumar Tagore ; Ayushman Singh ; Sumanth Manche ; Pratik Chattopadhyay
HIGHLIGHT:      In this paper, we propose an efficient hierarchical re-identification approach in which color histogram based comparison is first employed to find the closest matches in the gallery set, and next deep feature based comparison is carried out using Siamese network.

12, TITLE:      YANG2UML: Bijective Transformation and Simplification of YANG to UML
http://arxiv.org/abs/2005.03292
AUTHORS:        Mario Golling ; Robert Koch ; Peter Hillmann ; Rick Hofstede ; Frank Tietze
HIGHLIGHT:      In order to support this process, this paper presents an novel approach to optimize and simplify YANG data models to assist further discussions with the management and implementations (especially of interfaces) to reduce complexity.

13, TITLE:      Weakly-Supervised Neural Response Selection from an Ensemble of Task-Specialised Dialogue Agents
http://arxiv.org/abs/2005.03066
AUTHORS:        Asir Saeed ; Khai Mai ; Pham Minh ; Nguyen Tuan Duc ; Danushka Bollegala
HIGHLIGHT:      We model the problem of selecting the best response from a set of responses generated by a heterogeneous set of dialogue agents by taking into account the conversational history, and propose a \emph{Neural Response Selection} method.

14, TITLE:      Learning, transferring, and recommending performance knowledge with Monte Carlo tree search and neural networks
http://arxiv.org/abs/2005.03063
AUTHORS:        Don M. Dini
COMMENTS:       8 pages, 2 figures
HIGHLIGHT:      In this paper, a learning system is introduced that provides AI assistance for finding recommended changes to a program.

15, TITLE:      Categorical Vector Space Semantics for Lambek Calculus with a Relevant Modality
http://arxiv.org/abs/2005.03074
AUTHORS:        Lachlan McPheat ; Mehrnoosh Sadrzadeh ; Hadi Wazni ; Gijs Wijnholds
HIGHLIGHT:      We apply the model to construct categorical and concrete semantic interpretations for the motivating example of !

16, TITLE:      Diagnosing the Environment Bias in Vision-and-Language Navigation
http://arxiv.org/abs/2005.03086
AUTHORS:        Yubo Zhang ; Hao Tan ; Mohit Bansal
COMMENTS:       IJCAI 2020 (9 pages; first two authors contributed equally)
HIGHLIGHT:      In this work, we design novel diagnosis experiments via environment re-splitting and feature replacement, looking into possible reasons for this environment bias.

17, TITLE:      Line Artefact Quantification in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularisation
http://arxiv.org/abs/2005.03080
AUTHORS:        Oktay Karakuş ; Nantheera Anantrasirichai ; Amazigh Aguersif ; Stein Silva ; Adrian Basarab ; Alin Achim
COMMENTS:       15 pages, 9 figures
HIGHLIGHT:      In this paper, we present a novel method for line artefacts quantification in lung ultrasound (LUS) images of COVID-19 patients.

18, TITLE:      Inference with Choice Functions Made Practical
http://arxiv.org/abs/2005.03098
AUTHORS:        Arne Decadt ; Jasper De Bock ; Gert de Cooman
HIGHLIGHT:      We present a practical algorithm to compute this natural extension and provide several methods that can be used to improve its scalability.

19, TITLE:      A Multifactorial Optimization Paradigm for Linkage Tree Genetic Algorithm
http://arxiv.org/abs/2005.03090
AUTHORS:        Huynh Thi Thanh Binh ; Pham Dinh Thanh ; Tran Ba Trung ; Le Cong Thanh ; Le Minh Hai Phong ; Ananthram Swami ; Bui Thu Lam
HIGHLIGHT:      In this paper, we introduce Multifactorial Linkage Tree Genetic Algorithm (MF-LTGA) by combining the main features of both LTGA and MFO.

20, TITLE:      Technical Report of "Deductive Joint Support for Rational Unrestricted Rebuttal"
http://arxiv.org/abs/2005.03620
AUTHORS:        Marcos Cramer ; Meghna Bhadra
HIGHLIGHT:      In this paper we propose that ASPIC-style argumentation can benefit from keeping track not only of the attack relation between arguments, but also the relation of deductive joint support that holds between a set of arguments and an argument that was constructed from that set using a strict rule.

21, TITLE:      Seismic Shot Gather Noise Localization Using a Multi-Scale Feature-Fusion-Based Neural Network
http://arxiv.org/abs/2005.03626
AUTHORS:        Antonio José G. Busson ; Sérgio Colcher ; Ruy Luiz Milidiú ; Bruno Pereira Dias ; André Bulcão
HIGHLIGHT:      This letter presents an investigation on the effectiveness of a multi-scale feature-fusion-based network for seismic shot-gather noise localization.

22, TITLE:      Learning Robust Models for e-Commerce Product Search
http://arxiv.org/abs/2005.03624
AUTHORS:        Thanh V. Nguyen ; Nikhil Rao ; Karthik Subbian
COMMENTS:       This work has been accepted for publication at ACL2020
HIGHLIGHT:      In this paper, we develop a deep, end-to-end model that learns to effectively classify mismatches and to generate hard mismatched examples to improve the classifier.

23, TITLE:      Visualisation and knowledge discovery from interpretable models
http://arxiv.org/abs/2005.03632
AUTHORS:        Sreejita Ghosh ; Peter Tino ; Kerstin Bunte
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this contribution we introduced a few intrinsically interpretable models which are also capable of dealing with missing values, in addition to extracting knowledge from the dataset and about the problem.

24, TITLE:      On Exposure Bias, Hallucination and Domain Shift in Neural Machine Translation
http://arxiv.org/abs/2005.03642
AUTHORS:        Chaojun Wang ; Rico Sennrich
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we link exposure bias to another well-known problem in NMT, namely the tendency to generate hallucinations under domain shift.

25, TITLE:      Where is Linked Data in Question Answering over Linked Data?
http://arxiv.org/abs/2005.03640
AUTHORS:        Tommaso Soru ; Edgard Marx ; André Valdestilhas ; Diego Moussallem ; Gustavo Publio ; Muhammad Saleem
COMMENTS:       Position paper, THE Workshop @ ISWC 2018
HIGHLIGHT:      To this end, we propose the creation of new evaluation settings to leverage the advantages of the Semantic Web to achieve AI-complete question answering.

26, TITLE:      Plan2Vec: Unsupervised Representation Learning by Latent Plans
http://arxiv.org/abs/2005.03648
AUTHORS:        Ge Yang ; Amy Zhang ; Ari S. Morcos ; Joelle Pineau ; Pieter Abbeel ; Roberto Calandra
COMMENTS:       code available at https://geyang.github.io/plan2vec
HIGHLIGHT:      In this paper we introduce plan2vec, an unsupervised representation learning approach that is inspired by reinforcement learning.

27, TITLE:      Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan
http://arxiv.org/abs/2005.03405
AUTHORS:        Xiaofeng Zhu ; Bin Song ; Feng Shi ; Yanbo Chen ; Rongyao Hu ; Jiangzhang Gan ; Wenhai Zhang ; Man Li ; Liye Wang ; Yaozong Gao ; Fei Shan ; Dinggang Shen
HIGHLIGHT:      In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage.

28, TITLE:      The Perceptimatic English Benchmark for Speech Perception Models
http://arxiv.org/abs/2005.03418
AUTHORS:        Juliette Millet ; Ewan Dunbar
COMMENTS:       Accepted to CogSci Conference 2020
HIGHLIGHT:      We present the Perceptimatic English Benchmark, an open experimental benchmark for evaluating quantitative models of speech perception in English.

29, TITLE:      Kunster -- AR Art Video Maker -- Real time video neural style transfer on mobile devices
http://arxiv.org/abs/2005.03415
AUTHORS:        Wojciech Dudzik ; Damian Kosowski
HIGHLIGHT:      In this work, we present a solution to both of these problems.

30, TITLE:      Deep Learning on Point Clouds for False Positive Reduction at Nodule Detection in Chest CT Scans
http://arxiv.org/abs/2005.03654
AUTHORS:        Ivan Drokin ; Elena Ericheva
HIGHLIGHT:      We propose an algorithm for transforming 3d CT scan data to point cloud.

31, TITLE:      NTIRE 2020 Challenge on Spectral Reconstruction from an RGB Image
http://arxiv.org/abs/2005.03412
AUTHORS:        Boaz Arad ; Radu Timofte ; Ohad Ben-Shahar ; Yi-Tun Lin ; Graham Finlayson ; Shai Givati ;  others
HIGHLIGHT:      This paper reviews the second challenge on spectral reconstruction from RGB images, i.e., the recovery of whole-scene hyperspectral (HS) information from a 3-channel RGB image.

32, TITLE:      Fine-Grained Analysis of Cross-Linguistic Syntactic Divergences
http://arxiv.org/abs/2005.03436
AUTHORS:        Dmitry Nikolaev ; Ofir Arviv ; Taelin Karidi ; Neta Kenneth ; Veronika Mitnik ; Lilja Maria Saeboe ; Omri Abend
HIGHLIGHT:      We propose a framework for extracting divergence patterns for any language pair from a parallel corpus, building on Universal Dependencies. We further present a novel dataset, a manually word-aligned subset of the Parallel UD corpus in five languages, and use it to perform a detailed corpus study.

33, TITLE:      What comprises a good talking-head video generation?: A Survey and Benchmark
http://arxiv.org/abs/2005.03201
AUTHORS:        Lele Chen ; Guofeng Cui ; Ziyi Kou ; Haitian Zheng ; Chenliang Xu
HIGHLIGHT:      In this work, we present a carefully-designed benchmark for evaluating talking-head video generation with standardized dataset pre-processing strategies.

34, TITLE:      Hierarchical Attention Network for Action Segmentation
http://arxiv.org/abs/2005.03209
AUTHORS:        Harshala Gammulle ; Simon Denman ; Sridha Sridharan ; Clinton Fookes
COMMENTS:       Published in Pattern Recognition Letters
HIGHLIGHT:      To this end we propose a complete end-to-end supervised learning approach that can better learn relationships between actions over time, thus improving the overall segmentation performance.

35, TITLE:      NTIRE 2020 Challenge on NonHomogeneous Dehazing
http://arxiv.org/abs/2005.03457
AUTHORS:        Codruta O. Ancuti ; Cosmin Ancuti ; Florin-Alexandru Vasluianu ; Radu Timofte ; Jing Liu ; Haiyan Wu ; Yuan Xie ; Yanyun Qu ; Lizhuang Ma ; Ziling Huang ; Qili Deng ; Ju-Chin Chao ; Tsung-Shan Yang ; Peng-Wen Chen ; Po-Min Hsu ; Tzu-Yi Liao ; Chung-En Sun ; Pei-Yuan Wu ; Jeonghyeok Do ; Jongmin Park ; Munchurl Kim ; Kareem Metwaly ; Xuelu Li ; Tiantong Guo ; Vishal Monga ; Mingzhao Yu ; Venkateswararao Cherukuri ; Shiue-Yuan Chuang ; Tsung-Nan Lin ; David Lee ; Jerome Chang ; Zhan-Han Wang ; Yu-Bang Chang ; Chang-Hong Lin ; Yu Dong ; Hongyu Zhou ; Xiangzhen Kong ; Sourya Dipta Das ; Saikat Dutta ; Xuan Zhao ; Bing Ouyang ; Dennis Estrada ; Meiqi Wang ; Tianqi Su ; Siyi Chen ; Bangyong Sun ; Vincent Whannou de Dravo ; Zhe Yu ; Pratik Narang ; Aryan Mehra ; Navaneeth Raghunath ; Murari Mandal
COMMENTS:       CVPR Workshops Proceedings 2020
HIGHLIGHT:      This paper reviews the NTIRE 2020 Challenge on NonHomogeneous Dehazing of images (restoration of rich details in hazy image).

36, TITLE:      End-to-End Domain Adaptive Attention Network for Cross-Domain Person Re-Identification
http://arxiv.org/abs/2005.03222
AUTHORS:        Amena Khatun ; Simon Denman ; Sridha Sridharan ; Clinton Fookes
COMMENTS:       submitted to IEEE Transactions on Information Forensics and Security
HIGHLIGHT:      To address the on-going challenges regarding model generalisation, we propose an end-to-end domain adaptive attention network to jointly translate images between domains and learn discriminative re-id features in a single framework.

37, TITLE:      Deep Learning Framework for Detecting Ground Deformation in the Built Environment using Satellite InSAR data
http://arxiv.org/abs/2005.03221
AUTHORS:        Nantheera Anantrasirichai ; Juliet Biggs ; Krisztina Kelevitz ; Zahra Sadeghi ; Tim Wright ; James Thompson ; Alin Achim ; David Bull
HIGHLIGHT:      We propose three enhancement methods to tackle these problems: i) spatial interpolation with modified matrix completion, ii) a synthetic training dataset based on the characteristics of real UK velocity map, and iii) enhanced over-wrapping techniques.

38, TITLE:      How Can CNNs Use Image Position for Segmentation?
http://arxiv.org/abs/2005.03463
AUTHORS:        Rito Murase ; Masanori Suganuma ; Takayuki Okatani
COMMENTS:       11 pages
HIGHLIGHT:      In this study, we investigate how positional information is and can be utilized for segmentation tasks.

39, TITLE:      Deeply Supervised Active Learning for Finger Bones Segmentation
http://arxiv.org/abs/2005.03225
AUTHORS:        Ziyuan Zhao ; Xiaoyan Yang ; Bharadwaj Veeravalli ; Zeng Zeng
COMMENTS:       Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
HIGHLIGHT:      In this paper, we introduce a novel deeply supervised active learning approach for finger bones segmentation.

40, TITLE:      Multi-Target Deep Learning for Algal Detection and Classification
http://arxiv.org/abs/2005.03232
AUTHORS:        Peisheng Qian ; Ziyuan Zhao ; Haobing Liu ; Yingcai Wang ; Yu Peng ; Sheng Hu ; Jing Zhang ; Yue Deng ; Zeng Zeng
COMMENTS:       Accepted version to be published in the 42nd IEEE Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2020, Montreal, Canada
HIGHLIGHT:      In this paper, we propose a novel multi-target deep learning framework for algal detection and classification.

41, TITLE:      Hierarchical Predictive Coding Models in a Deep-Learning Framework
http://arxiv.org/abs/2005.03230
AUTHORS:        Matin Hosseini ; Anthony Maida
HIGHLIGHT:      Our review analyzes module connectivity and patterns of information transfer, seeking to find general principles used across the models.

42, TITLE:      2kenize: Tying Subword Sequences for Chinese Script Conversion
http://arxiv.org/abs/2005.03375
AUTHORS:        Pranav A ; Isabelle Augenstein
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      Here, we propose a model that can disambiguate between mappings and convert between the two scripts. We further construct benchmark datasets for topic classification and script conversion.

43, TITLE:      Playing Minecraft with Behavioural Cloning
http://arxiv.org/abs/2005.03374
AUTHORS:        Anssi Kanervisto ; Janne Karttunen ; Ville Hautamäki
COMMENTS:       To appear in Post Proceedings of the Competitions & Demonstrations Track @ NeurIPS2019. Source code available at https://github.com/Miffyli/minecraft-bc
HIGHLIGHT:      In this paper, we detail our submission to the competition, run further experiments to study how performance varied over training and study how different engineering decisions affected these results.

44, TITLE:      Vid2Curve: Simultaneously Camera Motion Estimation and Thin Structure Reconstruction from an RGB Video
http://arxiv.org/abs/2005.03372
AUTHORS:        Peng Wang ; Lingjie Liu ; Nenglun Chen ; Hung-Kuo Chu ; Christian Theobalt ; Wenping Wang
COMMENTS:       Accepted by SIGGRAPH 2020
HIGHLIGHT:      Specifically, we present a new curve-based approach to estimate accurate camera poses by establishing correspondences between featureless thin objects in the foreground in consecutive video frames, without requiring visual texture in the background scene to lock on.

45, TITLE:      Semantic Signatures for Large-scale Visual Localization
http://arxiv.org/abs/2005.03388
AUTHORS:        Li Weng ; Valerie Gouet-Brunet ; Bahman Soheilian
COMMENTS:       12 pages, 22 figures, submitted to Multimedia Tools and Applications
HIGHLIGHT:      These approaches offer good accuracy but suffer from scalability issues.

46, TITLE:      WSMN: An optimized multipurpose blind watermarking in Shearlet domain using MLP and NSGA-II
http://arxiv.org/abs/2005.03382
AUTHORS:        Behrouz Bolourian Haghighi ; Amir Hossein Taherinia ; Ahad Harati ; Modjtaba Rouhani
HIGHLIGHT:      Hence, this paper presents an optimized multipurpose blind watermarking in Shearlet domain with the help of smart algorithms including MLP and NSGA-II.

47, TITLE:      NTIRE 2020 Challenge on Image Demoireing: Methods and Results
http://arxiv.org/abs/2005.03155
AUTHORS:        Shanxin Yuan ; Radu Timofte ; Ales Leonardis ; Gregory Slabaugh ; Xiaotong Luo ; Jiangtao Zhang ; Yanyun Qu ; Ming Hong ; Yuan Xie ; Cuihua Li ; Dejia Xu ; Yihao Chu ; Qingyan Sun ; Shuai Liu ; Ziyao Zong ; Nan Nan ; Chenghua Li ; Sangmin Kim ; Hyungjoon Nam ; Jisu Kim ; Jechang Jeong ; Manri Cheon ; Sung-Jun Yoon ; Byungyeon Kang ; Junwoo Lee ; Bolun Zheng ; Xiaohong Liu ; Linhui Dai ; Jun Chen ; Xi Cheng ; Zhenyong Fu ; Jian Yang ; Chul Lee ; An Gia Vien ; Hyunkook Park ; Sabari Nathan ; M. Parisa Beham ; S Mohamed Mansoor Roomi ; Florian Lemarchand ; Maxime Pelcat ; Erwan Nogues ; Densen Puthussery ; Hrishikesh P S ; Jiji C V ; Ashish Sinha ; Xuan Zhao
HIGHLIGHT:      This paper reviews the Challenge on Image Demoireing that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2020.

48, TITLE:      Does Multi-Encoder Help? A Case Study on Context-Aware Neural Machine Translation
http://arxiv.org/abs/2005.03393
AUTHORS:        Bei Li ; Hui Liu ; Ziyang Wang ; Yufan Jiang ; Tong Xiao ; Jingbo Zhu ; Tongran Liu ; Changliang Li
COMMENTS:       5 pages, 2 figures, 5 tables, accpeted by ACL2020
HIGHLIGHT:      In this paper, we investigate multi-encoder approaches in documentlevel neural machine translation (NMT).

49, TITLE:      A Parameterized Perspective on Attacking and Defending Elections
http://arxiv.org/abs/2005.03176
AUTHORS:        Kishen N. Gowda ; Neeldhara Misra ; Vraj Patel
HIGHLIGHT:      We study these problems from a parameterized perspective with the goal of establishing a more detailed complexity landscape.

50, TITLE:      Fact-based Dialogue Generation with Convergent and Divergent Decoding
http://arxiv.org/abs/2005.03174
AUTHORS:        Ryota Tanaka ; Akinobu Lee
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      Various methods were proposed to focus on generating informative words that contain facts effectively.

51, TITLE:      An Optimal Control Theory for the Traveling Salesman Problem and Its Variants
http://arxiv.org/abs/2005.03186
AUTHORS:        I. M. Ross ; R. J. Proulx ; M. Karpenko
COMMENTS:       24 pages, 8 figures
HIGHLIGHT:      In sharp contrast to their discrete-optimization counterparts, the modeling constructs presented in this paper represent a fundamentally new domain of analysis and computation for TSPs and their variants.

52, TITLE:      A Proposal for Intelligent Agents with Episodic Memory
http://arxiv.org/abs/2005.03182
AUTHORS:        David Murphy ; Thomas S. Paula ; Wagston Staehler ; Juliano Vacaro ; Gabriel Paz ; Guilherme Marques ; Bruna Oliveira
COMMENTS:       7 pages, 2 figures
HIGHLIGHT:      In this short paper, we propose one potential approach to provide an AI agent with such capabilities.

53, TITLE:      Evolutionary Multi Objective Optimization Algorithm for Community Detection in Complex Social Networks
http://arxiv.org/abs/2005.03181
AUTHORS:        Shaik Tanveer ul Huq ; Vadlamani Ravi ; Kalyanmoy Deb
COMMENTS:       33 pages, 20 figures
HIGHLIGHT:      In this paper, we propose two variants of a three-objective formulation using a customized non-dominated sorting genetic algorithm III (NSGA-III) to find community structures in a network.

54, TITLE:      Recognizing Exercises and Counting Repetitions in Real Time
http://arxiv.org/abs/2005.03194
AUTHORS:        Talal Alatiah ; Chen Chen
HIGHLIGHT:      In this project, pose estimation and deep machine learning techniques are combined to analyze the performance and report feedback on the repetitions of performed exercises in real-time.

55, TITLE:      Trains, Games, and Complexity: 0/1/2-Player Motion Planning through Input/Output Gadgets
http://arxiv.org/abs/2005.03192
AUTHORS:        Joshua Ani ; Erik D. Demaine ; Dylan H. Hendrickson ; Jayson Lynch
COMMENTS:       37 pages, 36 figures
HIGHLIGHT:      We analyze the computational complexity of motion planning through local "input/output" gadgets with separate entrances and exits, and a subset of allowed traversals from entrances to exits, each of which changes the state of the gadget and thereby the allowed traversals.

56, TITLE:      ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
http://arxiv.org/abs/2005.03191
AUTHORS:        Wei Han ; Zhengdong Zhang ; Yu Zhang ; Jiahui Yu ; Chung-Cheng Chiu ; James Qin ; Anmol Gulati ; Ruoming Pang ; Yonghui Wu
HIGHLIGHT:      In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet.

57, TITLE:      A Dynamical Perspective on Point Cloud Registration
http://arxiv.org/abs/2005.03190
AUTHORS:        Heng Yang
COMMENTS:       Preliminary results, 10 pages, 4 figures
HIGHLIGHT:      We provide a dynamical perspective on the classical problem of 3D point cloud registration with correspondences.

58, TITLE:      Heidelberg Colorectal Data Set for Surgical Data Science in the Sensor Operating Room
http://arxiv.org/abs/2005.03501
AUTHORS:        Lena Maier-Hein ; Martin Wagner ; Tobias Ross ; Annika Reinke ; Sebastian Bodenstedt ; Peter M. Full ; Hellena Hempe ; Diana Mindroc-Filimon ; Patrick Scholz ; Thuy Nuong Tran ; Pierangela Bruno ; Anna Kisilenko ; Benjamin Müller ; Tornike Davitashvili ; Manuela Capek ; Minu Tizabi ; Matthias Eisenmann ; Tim J. Adler ; Janek Gröhl ; Melanie Schellenberg ; Silvia Seidlitz ; T. Y. Emmy Lai ; Veith Roethlingshoefer ; Fabian Both ; Sebastian Bittel ; Marc Mengler ; Martin Apitz ; Stefanie Speidel ; Hannes G. Kenngott ; Beat P. Müller-Stich
COMMENTS:       Submitted to Nature Scientific Data
HIGHLIGHT:      This paper introduces the Heidelberg Colorectal (HeiCo) data set - the first publicly available data set enabling comprehensive benchmarking of medical instrument detection and segmentation algorithms with a specific emphasis on robustness and generalization capabilities of the methods.

59, TITLE:      The Danish Gigaword Project
http://arxiv.org/abs/2005.03521
AUTHORS:        Leon Strømberg-Derczynski ; Rebekah Baglini ; Morten H. Christiansen ; Manuel R. Ciosici ; Jacob Aarup Dalsgaard ; Riccardo Fusaroli ; Peter Juel Henrichsen ; Rasmus Hvingelby ; Andreas Kirkedal ; Alex Speed Kjeldsen ; Claus Ladefoged ; Finn Årup Nielsen ; Malte Lau Petersen ; Jonathan Hvithamar Rystrøm ; Daniel Varab
HIGHLIGHT:      This paper describes the Danish Gigaword project, which aims to construct a freely-available one billion word corpus of Danish text that represents the breadth of the written language.

60, TITLE:      CounQER: A System for Discovering and Linking Count Information in Knowledge Bases
http://arxiv.org/abs/2005.03529
AUTHORS:        Shrestha Ghosh ; Simon Razniewski ; Gerhard Weikum
COMMENTS:       Accepted at ESWC 2020
HIGHLIGHT:      CounQER: A System for Discovering and Linking Count Information in Knowledge Bases

61, TITLE:      MISA: Modality-Invariant and -Specific Representations for Multimodal Sentiment Analysis
http://arxiv.org/abs/2005.03545
AUTHORS:        Devamanyu Hazarika ; Roger Zimmermann ; Soujanya Poria
HIGHLIGHT:      In this paper, we aim to learn effective modality representations to aid the process of fusion.

62, TITLE:      Nakdan: Professional Hebrew Diacritizer
http://arxiv.org/abs/2005.03312
AUTHORS:        Avi Shmidman ; Shaltiel Shmidman ; Moshe Koppel ; Yoav Goldberg
COMMENTS:       Accepted to ACL 2020, System Demonstrations
HIGHLIGHT:      We present a system for automatic diacritization of Hebrew text.

63, TITLE:      A Review of Computer Vision Methods in Network Security
http://arxiv.org/abs/2005.03318
AUTHORS:        Jiawei Zhao ; Rahat Masood ; Suranga Seneviratne
HIGHLIGHT:      In this paper, we provide a comprehensive survey of such work under three topics; i) phishing attempt detection, ii) malware detection, and iii) traffic anomaly detection.

64, TITLE:      Encoding in the Dark Grand Challenge: An Overview
http://arxiv.org/abs/2005.03315
AUTHORS:        Nantheera Anantrasirichai ; Fan Zhang ; Alexandra Malyugina ; Paul Hill ; Angeliki Katsenou
HIGHLIGHT:      In this paper, we present an overview of the proposed challenge, and test state-of-the-art methods that will be part of the benchmark methods at the stage of the participants' deliverable assessment.

65, TITLE:      NH-HAZE: An Image Dehazing Benchmark with Non-Homogeneous Hazy and Haze-Free Images
http://arxiv.org/abs/2005.03560
AUTHORS:        Codruta O. Ancuti ; Cosmin Ancuti ; Radu Timofte
COMMENTS:       CVPR 2020 Workshops proceedings
HIGHLIGHT:      Since in many real cases haze is not uniformly distributed we introduce NH-HAZE, a non-homogeneous realistic dataset with pairs of real hazy and corresponding haze-free images.

66, TITLE:      Enhancing Geometric Factors in Model Learning and Inference for Object Detection and Instance Segmentation
http://arxiv.org/abs/2005.03572
AUTHORS:        Zhaohui Zheng ; Ping Wang ; Dongwei Ren ; Wei Liu ; Rongguang Ye ; Qinghua Hu ; Wangmeng Zuo
COMMENTS:       All the source code and trained models are available at https://github.com/Zzh-tju/CIoU arXiv admin note: text overlap with arXiv:1911.08287
HIGHLIGHT:      In this paper, we propose Complete-IoU (CIoU) loss and Cluster-NMS for enhancing geometric factors in both bounding box regression and Non-Maximum Suppression (NMS), leading to notable gains of average precision (AP) and average recall (AR), without the sacrifice of inference efficiency.

67, TITLE:      Wavelet Integrated CNNs for Noise-Robust Image Classification
http://arxiv.org/abs/2005.03337
AUTHORS:        Qiufu Li ; Linlin Shen ; Sheng Guo ; Zhihui Lai
COMMENTS:       CVPR accepted paper
HIGHLIGHT:      We present general DWT and Inverse DWT (IDWT) layers applicable to various wavelets like Haar, Daubechies, and Cohen, etc., and design wavelet integrated CNNs (WaveCNets) using these layers for image classification.

68, TITLE:      Regression Forest-Based Atlas Localization and Direction Specific Atlas Generation for Pancreas Segmentation
http://arxiv.org/abs/2005.03345
AUTHORS:        Masahiro Oda ; Natsuki Shimizu ; Ken'ichi Karasawa ; Yukitaka Nimura ; Takayuki Kitasaka ; Kazunari Misawa ; Michitaka Fujiwara ; Daniel Rueckert ; Kensaku Mori
COMMENTS:       Accepted paper as a poster presentation at MICCAI 2016 (International Conference on Medical Image Computing and Computer-Assisted Intervention), Athens, Greece
HIGHLIGHT:      This paper proposes a fully automated atlas-based pancreas segmentation method from CT volumes utilizing atlas localization by regression forest and atlas generation using blood vessel information.

69, TITLE:      Scale-Equalizing Pyramid Convolution for Object Detection
http://arxiv.org/abs/2005.03101
AUTHORS:        Xinjiang Wang ; Shilong Zhang ; Zhuoran Yu ; Litong Feng ; Wayne Zhang
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      Inspired by this, a convolution across the pyramid level is proposed in this study, which is termed pyramid convolution and is a modified 3-D convolution.

70, TITLE:      Scene Text Image Super-Resolution in the Wild
http://arxiv.org/abs/2005.03341
AUTHORS:        Wenjia Wang ; Enze Xie ; Xuebo Liu ; Wenhai Wang ; Ding Liang ; Chunhua Shen ; Xiang Bai
HIGHLIGHT:      In this purpose, a new Text Super-Resolution Network termed TSRN, with three novel modules is developed.

71, TITLE:      Deep Learning for Image-based Automatic Dial Meter Reading: Dataset and Baselines
http://arxiv.org/abs/2005.03106
AUTHORS:        Gabriel Salomon ; Rayson Laroca ; David Menotti
COMMENTS:       Accepted for presentation at the 2020 International Joint Conference on Neural Networks (IJCNN)
HIGHLIGHT:      Our main contributions are: (i) a public real-world dial meter dataset (shared upon request) called UFPR-ADMR; (ii) a deep learning-based recognition baseline on the proposed dataset; and (iii) a detailed error analysis of the main issues present in AMR for dial meters.

72, TITLE:      Learning Implicit Text Generation via Feature Matching
http://arxiv.org/abs/2005.03588
AUTHORS:        Inkit Padhi ; Pierre Dognin ; Ke Bai ; Cicero Nogueira dos Santos ; Vijil Chenthamarakshan ; Youssef Mroueh ; Payel Das
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we present new GFMN formulations that are effective for sequential data.

73, TITLE:      DramaQA: Character-Centered Video Story Understanding with Hierarchical QA
http://arxiv.org/abs/2005.03356
AUTHORS:        Seongho Choi ; Kyoung-Woon On ; Yu-Jung Heo ; Ahjeong Seo ; Youwon Jang ; Seungchan Lee ; Minsu Lee ; Byoung-Tak Zhang
COMMENTS:       21 pages, 10 figures, submitted to ECCV 2020
HIGHLIGHT:      In this paper, we propose a novel video question answering (Video QA) task, DramaQA, for a comprehensive understanding of the video story.

74, TITLE:      Efficient Exact Verification of Binarized Neural Networks
http://arxiv.org/abs/2005.03597
AUTHORS:        Kai Jia ; Martin Rinard
HIGHLIGHT:      We present a new system, EEV, for verifying binarized neural networks (BNNs).

75, TITLE:      DMCP: Differentiable Markov Channel Pruning for Neural Networks
http://arxiv.org/abs/2005.03354
AUTHORS:        Shaopeng Guo ; Yujie Wang ; Quanquan Li ; Junjie Yan
COMMENTS:       CVPR2020 Oral. Code has been released at https://github.com/zx55/dmcp
HIGHLIGHT:      In this paper, we propose a novel differentiable method for channel pruning, named Differentiable Markov Channel Pruning (DMCP), to efficiently search the optimal sub-structure.

76, TITLE:      A Tale of Two Perplexities: Sensitivity of Neural Language Models to Lexical Retrieval Deficits in Dementia of the Alzheimer's Type
http://arxiv.org/abs/2005.03593
AUTHORS:        Trevor Cohen ; Serguei Pakhomov
COMMENTS:       To be published in the Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      In this paper, we interrogate neural LMs trained on participants with and without dementia using synthetic narratives previously developed to simulate progressive semantic dementia by manipulating lexical frequency.

77, TITLE:      A Locally Adaptive Interpretable Regression
http://arxiv.org/abs/2005.03350
AUTHORS:        Lkhagvadorj Munkhdalai ; Tsendsuren Munkhdalai ; Keun Ho Ryu
HIGHLIGHT:      In this work, we introduce a locally adaptive interpretable regression (LoAIR).

78, TITLE:      Unsupervised Multimodal Neural Machine Translation with Pseudo Visual Pivoting
http://arxiv.org/abs/2005.03119
AUTHORS:        Po-Yao Huang ; Junjie Hu ; Xiaojun Chang ; Alexander Hauptmann
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we investigate how to utilize visual content for disambiguation and promoting latent space alignment in unsupervised MMT.

79, TITLE:      Self-Supervised Human Depth Estimation from Monocular Videos
http://arxiv.org/abs/2005.03358
AUTHORS:        Feitong Tan ; Hao Zhu ; Zhaopeng Cui ; Siyu Zhu ; Marc Pollefeys ; Ping Tan
COMMENTS:       Accepted by IEEE Conference on Computer Vision and Patten Recognition (CVPR), 2020
HIGHLIGHT:      This paper presents a self-supervised method that can be trained on YouTube videos without known depth, which makes training data collection simple and improves the generalization of the learned network.

80, TITLE:      Scoring Root Necrosis in Cassava Using Semantic Segmentation
http://arxiv.org/abs/2005.03367
AUTHORS:        Jeremy Francis Tusubira ; Benjamin Akera ; Solomon Nsumba ; Joyce Nakatumba-Nabende ; Ernest Mwebaze
COMMENTS:       15 pages, 5 figures
HIGHLIGHT:      In this paper we present an approach to automate root necrosis scoring using deep convolutional neural networks with semantic segmentation.

81, TITLE:      Rigid Matrices From Rectangular PCPs
http://arxiv.org/abs/2005.03123
AUTHORS:        Amey Bhangale ; Prahladh Harsha ; Orr Paradise ; Avishay Tal
COMMENTS:       34 pages
HIGHLIGHT:      We introduce a variant of PCPs, that we refer to as rectangular PCPs, wherein proofs are thought of as square matrices, and the random coins used by the verifier can be partitioned into two disjoint sets, one determining the row of each query and the other determining the *column*.

82, TITLE:      JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
http://arxiv.org/abs/2005.03361
AUTHORS:        Zhuoyuan Mao ; Fabien Cromieres ; Raj Dabre ; Haiyue Song ; Sadao Kurohashi
COMMENTS:       LREC 2020
HIGHLIGHT:      To this end, we propose JASS, Japanese-specific Sequence to Sequence, as a novel pre-training alternative to MASS for NMT involving Japanese as the source or target language. We will release our code, pre-trained models and bunsetsu annotated data as resources for researchers to use in their own NLP tasks.



==========Updates to Previous Papers==========
1, TITLE:       A memory of motion for visual predictive control tasks
http://arxiv.org/abs/2001.11759
AUTHORS:        Antonio Paolillo ; Teguh Santoso Lembono ; Sylvain Calinon
COMMENTS:       7 pages
HIGHLIGHT:      This paper addresses the problem of efficiently achieving visual predictive control tasks.

2, TITLE:       Global Locality in Biomedical Relation and Event Extraction
http://arxiv.org/abs/1909.04822
AUTHORS:        Elaheh ShafieiBavani ; Antonio Jimeno Yepes ; Xu Zhong ; David Martinez Iraola
COMMENTS:       10 pages. arXiv admin note: text overlap with arXiv:1802.10569, arXiv:1710.08312 by other authors
HIGHLIGHT:      We propose an approach to both relation and event extraction, for simultaneously predicting relationships between all mention pairs in a text.

3, TITLE:       Jealousy-freeness and other common properties in Fair Division of Mixed Manna
http://arxiv.org/abs/2004.11469
AUTHORS:        Martin Aleksandrov
COMMENTS:       Propositions 1 and 5 contain stronger results. The paper has 13 pages, 1 table and 2 figures
HIGHLIGHT:      For this model, we study axiomatic concepts of allocations such as jealousy-freeness up to one item, envy-freeness up to one item and Pareto-optimality.

4, TITLE:       A Global Benchmark of Algorithms for Segmenting Late Gadolinium-Enhanced Cardiac Magnetic Resonance Imaging
http://arxiv.org/abs/2004.12314
AUTHORS:        Zhaohan Xiong ; Qing Xia ; Zhiqiang Hu ; Ning Huang ; Cheng Bian ; Yefeng Zheng ; Sulaiman Vesal ; Nishant Ravikumar ; Andreas Maier ; Xin Yang ; Pheng-Ann Heng ; Dong Ni ; Caizi Li ; Qianqian Tong ; Weixin Si ; Elodie Puybareau ; Younes Khoudli ; Thierry Geraud ; Chen Chen ; Wenjia Bai ; Daniel Rueckert ; Lingchao Xu ; Xiahai Zhuang ; Xinzhe Luo ; Shuman Jia ; Maxime Sermesant ; Yashu Liu ; Kuanquan Wang ; Davide Borra ; Alessandro Masci ; Cristiana Corsi ; Coen de Vente ; Mitko Veta ; Rashed Karim ; Chandrakanth Jayachandran Preetha ; Sandy Engelhardt ; Menyun Qiao ; Yuanyuan Wang ; Qian Tao ; Marta Nunez-Garcia ; Oscar Camara ; Nicolo Savioli ; Pablo Lamata ; Jichao Zhao
HIGHLIGHT:      In this paper, extensive analysis of the submitted algorithms using technical and biological metrics was performed by undergoing subgroup analysis and conducting hyper-parameter analysis, offering an overall picture of the major design choices of convolutional neural networks (CNNs) and practical considerations for achieving state-of-the-art left atrium segmentation.

5, TITLE:       Compression, inversion, and approximate PCA of dense kernel matrices at near-linear computational complexity
http://arxiv.org/abs/1706.02205
AUTHORS:        Florian Schäfer ; T. J. Sullivan ; Houman Owhadi
COMMENTS:       54 pages
HIGHLIGHT:      For covariance functions that are Green's functions of elliptic boundary value problems and homogeneously-distributed sampling points, we show how to identify a subset $S \subset \{ 1 , \dots , N \}^2$, with $\# S = O ( N \log (N) \log^{d} ( N /\epsilon ) )$, such that the zero fill-in incomplete Cholesky factorisation of the sparse matrix $\Theta_{ij} 1_{( i, j ) \in S}$ is an $\epsilon$-approximation of $\Theta$.

6, TITLE:       Toward Improving the Evaluation of Visual Attention Models: a Crowdsourcing Approach
http://arxiv.org/abs/2002.04407
AUTHORS:        Dario Zanca ; Stefano Melacci ; Marco Gori
HIGHLIGHT:      In this work we focus on the evaluation methodology of models of human visual attention.

7, TITLE:       Generating Thermal Image Data Samples using 3D Facial Modelling Techniques and Deep Learning Methodologies
http://arxiv.org/abs/2005.01923
AUTHORS:        Muhammad Ali Farooq ; Peter Corcoran
COMMENTS:       Paper accpeted in QOMEX IEEE 2020 Conference copyright submitted to IEEE
HIGHLIGHT:      In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models.

8, TITLE:       t-SS3: a text classifier with dynamic n-grams for early risk detection over text streams
http://arxiv.org/abs/1911.06147
AUTHORS:        Sergio G. Burdisso ; Marcelo Errecalde ; Manuel Montes-y-Gómez
COMMENTS:       Highlights: (*) A classifier that is able to dynamically learn and recognize important word n-grams. (*) A novel text classifier having the ability to visually explain its rationale. (*) Support for incremental learning and text classification over streams. (*) Efficient model for addressing early risk detection problems
HIGHLIGHT:      This paper introduces t-SS3, an extension of SS3 that allows it to recognize useful patterns over text streams dynamically.

9, TITLE:       Mnemonics Training: Multi-Class Incremental Learning without Forgetting
http://arxiv.org/abs/2002.10211
AUTHORS:        Yaoyao Liu ; An-An Liu ; Yuting Su ; Bernt Schiele ; Qianru Sun
COMMENTS:       Accepted by CVPR 2020. Code is available at https://github.com/yaoyao-liu/mnemonics
HIGHLIGHT:      This paper proposes a novel and automatic framework we call mnemonics, where we parameterize exemplars and make them optimizable in an end-to-end manner.

10, TITLE:      Cross-Lingual Semantic Role Labeling with High-Quality Translated Training Corpus
http://arxiv.org/abs/2004.06295
AUTHORS:        Hao Fei ; Meishan Zhang ; Donghong Ji
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we propose a novel alternative based on corpus translation, constructing high-quality training datasets for the target languages from the source gold-standard SRL annotations.

11, TITLE:      Lake Ice Detection from Sentinel-1 SAR with Deep Learning
http://arxiv.org/abs/2002.07040
AUTHORS:        Manu Tom ; Roberto Aguilar ; Pascal Imhof ; Silvan Leinss ; Emmanuel Baltsavias ; Konrad Schindler
COMMENTS:       Accepted for ISPRS Congress 2020, Nice, France
HIGHLIGHT:      We present a lake ice monitoring system based on the automatic analysis of Sentinel-1 Synthetic Aperture Radar (SAR) data with a deep neural network.

12, TITLE:      Unsupervised Domain Adaptation on Reading Comprehension
http://arxiv.org/abs/1911.06137
AUTHORS:        Yu Cao ; Meng Fang ; Baosheng Yu ; Joey Tianyi Zhou
COMMENTS:       9 pages, 6 figures, 5 tables, Accepted by AAAI 2020
HIGHLIGHT:      To solve this, we provide a novel conditional adversarial self-training method (CASe).

13, TITLE:      Improved RawNet with Feature Map Scaling for Text-independent Speaker Verification using Raw Waveforms
http://arxiv.org/abs/2004.00526
AUTHORS:        Jee-weon Jung ; Seung-bin Kim ; Hye-jin Shim ; Ju-ho Kim ; Ha-Jin Yu
COMMENTS:       5 pages, 1 figure, 5 tables, submitted to Interspeech 2020 as a conference paper
HIGHLIGHT:      In this study, we improve RawNet by scaling feature maps using various methods.

14, TITLE:      IPG-Net: Image Pyramid Guidance Network for Small Object Detection
http://arxiv.org/abs/1912.00632
AUTHORS:        Ziming Liu ; Guangyu Gao ; Lin Sun ; Li Fang
COMMENTS:       Accepted by CVPR2020 Anti-UVA workshop
HIGHLIGHT:      In this paper, except for top-down combining of information for shallow layers, we propose a novel network called Image Pyramid Guidance Network (IPG-Net) to make sure both the spatial information and semantic information are abundant for each layer.

15, TITLE:      Deblurring by Realistic Blurring
http://arxiv.org/abs/2004.01860
AUTHORS:        Kaihao Zhang ; Wenhan Luo ; Yiran Zhong ; Lin Ma ; Bjorn Stenger ; Wei Liu ; Hongdong Li
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      To address this problem, we propose a new method which combines two GAN models, i.e., a learning-to-Blur GAN (BGAN) and learning-to-DeBlur GAN (DBGAN), in order to learn a better model for image deblurring by primarily learning how to blur images. As an additional contribution, this paper also introduces a Real-World Blurred Image (RWBI) dataset including diverse blurry images.

16, TITLE:      Learning Direct Optimization for Scene Understanding
http://arxiv.org/abs/1812.07524
AUTHORS:        Lukasz Romaszko ; Christopher K. I. Williams ; John Winn
HIGHLIGHT:      Our goal is to explain a single image x with an interpretable 3D computer graphics model having scene graph latent variables z (such as object appearance, camera position).

17, TITLE:      SilhoNet: An RGB Method for 6D Object Pose Estimation
http://arxiv.org/abs/1809.06893
AUTHORS:        Gideon Billings ; Matthew Johnson-Roberson
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      In this work, we introduce a novel method called SilhoNet that predicts 6D object pose from monocular images.

18, TITLE:      SPECTER: Document-level Representation Learning using Citation-informed Transformers
http://arxiv.org/abs/2004.07180
AUTHORS:        Arman Cohan ; Sergey Feldman ; Iz Beltagy ; Doug Downey ; Daniel S. Weld
COMMENTS:       ACL 2020
HIGHLIGHT:      We propose SPECTER, a new method to generate document-level embedding of scientific documents based on pretraining a Transformer language model on a powerful signal of document-level relatedness: the citation graph.

19, TITLE:      Generative Adversarial Networks in Digital Pathology: A Survey on Trends and Future Potential
http://arxiv.org/abs/2004.14936
AUTHORS:        Maximilian Ernst Tschuchnig ; Gertie Janneke Oostingh ; Michael Gadermayr
HIGHLIGHT:      In this paper, we focus on a particularly powerful class of architectures, called Generative Adversarial Networks (GANs), applied to histological image data.

20, TITLE:      Restricting the Flow: Information Bottlenecks for Attribution
http://arxiv.org/abs/2001.00396
AUTHORS:        Karl Schulz ; Leon Sixt ; Federico Tombari ; Tim Landgraf
COMMENTS:       18 pages, 12 figures, accepted at ICLR 2020 (Oral)
HIGHLIGHT:      In this work we adapt the information bottleneck concept for attribution.

21, TITLE:      On-board Deep-learning-based Unmanned Aerial Vehicle Fault Cause Detection and Identification
http://arxiv.org/abs/2005.00336
AUTHORS:        Vidyasagar Sadhu ; Saman Zonouz ; Dario Pompili
COMMENTS:       IEEE International Conference on Robotics and Automation (ICRA), May 2020, 6+1 pages
HIGHLIGHT:      In this paper, we propose novel architectures based on deep Convolutional and Long Short-Term Memory Neural Networks (CNNs and LSTMs) to detect (via Autoencoder) and classify drone mis-operations based on sensor data.

22, TITLE:      The Cascade Transformer: an Application for Efficient Answer Sentence Selection
http://arxiv.org/abs/2005.02534
AUTHORS:        Luca Soldaini ; Alessandro Moschitti
COMMENTS:       Accepted to ACL 2020 (long)
HIGHLIGHT:      In this paper, we introduce the Cascade Transformer, a simple yet effective technique to adapt transformer-based models into a cascade of rankers.

23, TITLE:      Multi-task pre-training of deep neural networks for digital pathology
http://arxiv.org/abs/2005.02561
AUTHORS:        Romain Mormont ; Pierre Geurts ; Raphaël Marée
COMMENTS:       Accepted for publication in the IEEE Journal of Biomedical and Health Informatics, special issue on Computational Pathology
HIGHLIGHT:      In this work, we investigate multi-task learning as a way of pre-training models for classification tasks in digital pathology. We first assemble and transform many digital pathology datasets into a pool of 22 classification tasks and almost 900k images.

24, TITLE:      Ranked List Loss for Deep Metric Learning
http://arxiv.org/abs/1903.03238
AUTHORS:        Xinshao Wang ; Yang Hua ; Elyor Kodirov ; Neil M. Robertson
COMMENTS:       TPAMI Extension of CVPR-19, Deep Metric Learning, Learning Representations, Image Retrieval/Clustering, Code: https://github.com/XinshaoAmosWang/Ranked-List-Loss-for-DML
HIGHLIGHT:      In this work, we unveil two limitations of existing ranking-motivated structured losses and propose a novel ranked list loss to solve both of them.

25, TITLE:      Recurrent Neural Network Language Models Always Learn English-Like Relative Clause Attachment
http://arxiv.org/abs/2005.00165
AUTHORS:        Forrest Davis ; Marten van Schijndel
COMMENTS:       Proceedings of 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020); v3 updated references and added additional corpus stats in discussion
HIGHLIGHT:      A standard approach to evaluating language models analyzes how models assign probabilities to valid versus invalid syntactic constructions (i.e. is a grammatical sentence more probable than an ungrammatical sentence).

26, TITLE:      Parameterised Counting in Logspace
http://arxiv.org/abs/1904.12156
AUTHORS:        Anselm Haak ; Arne Meier ; Om Prakash ; Raghavendra Rao B. V
COMMENTS:       Changed title to give a better idea of the content at first glance, also many minor corrections and polishing
HIGHLIGHT:      In this article, we study the counting versions of such operators and introduce variants based on tail-nondeterminism, paraW[1] and paraBetaTail, in the setting of parameterised logarithmic space.

27, TITLE:      Dynamic Face Video Segmentation via Reinforcement Learning
http://arxiv.org/abs/1907.01296
AUTHORS:        Yujiang Wang ; Mingzhi Dong ; Jie Shen ; Yang Wu ; Shiyang Cheng ; Maja Pantic
COMMENTS:       CVPR 2020
HIGHLIGHT:      Some works used a fixed key scheduling policy, while others proposed adaptive key scheduling methods based on heuristic strategies, both of which may lead to suboptimal global performance.

28, TITLE:      Identifying Compromised Accounts on Social Media Using Statistical Text Analysis
http://arxiv.org/abs/1804.07247
AUTHORS:        Dominic Seyler ; Lunan Li ; ChengXiang Zhai
HIGHLIGHT:      We propose a novel general framework for discovering compromised accounts by semantic analysis of text messages coming out from an account.

29, TITLE:      SCAttNet: Semantic Segmentation Network with Spatial and Channel Attention Mechanism for High-Resolution Remote Sensing Images
http://arxiv.org/abs/1912.09121
AUTHORS:        Haifeng Li ; Kaijian Qiu ; Li Chen ; Xiaoming Mei ; Liang Hong ; Chao Tao
COMMENTS:       5 pages, 3 figures, 2 tables
HIGHLIGHT:      In this paper, we propose a new end-to-end semantic segmentation network, which integrates lightweight spatial and channel attention modules that can refine features adaptively.

30, TITLE:      Self-Attention with Cross-Lingual Position Representation
http://arxiv.org/abs/2004.13310
AUTHORS:        Liang Ding ; Longyue Wang ; Dacheng Tao
COMMENTS:       To appear in ACL 2020
HIGHLIGHT:      In this paper, we augment SANs with \emph{cross-lingual position representations} to model the bilingually aware latent structure for the input sentence.

31, TITLE:      Towards Embodied Scene Description
http://arxiv.org/abs/2004.14638
AUTHORS:        Sinan Tan ; Huaping Liu ; Di Guo ; Xinyu Zhang ; Fuchun Sun
COMMENTS:       Accepted in Robotics: Science and Systems 2020
HIGHLIGHT:      In this work, we propose the Embodied Scene Description, which exploits the embodiment ability of the agent to find an optimal viewpoint in its environment for scene description tasks.

32, TITLE:      Measuring Social Bias in Knowledge Graph Embeddings
http://arxiv.org/abs/1912.02761
AUTHORS:        Joseph Fisher ; Dave Palfrey ; Christos Christodoulopoulos ; Arpit Mittal
HIGHLIGHT:      We present the first study on social bias in knowledge graph embeddings, and propose a new metric suitable for measuring such bias.

33, TITLE:      GraCIAS: Grassmannian of Corrupted Images for Adversarial Security
http://arxiv.org/abs/2005.02936
AUTHORS:        Ankita Shukla ; Pavan Turaga ; Saket Anand
COMMENTS:       16 pages
HIGHLIGHT:      In this work, we propose a defense strategy that applies random image corruptions to the input image alone, constructs a self-correlation based subspace followed by a projection operation to suppress the adversarial perturbation.

34, TITLE:      Warwick Image Forensics Dataset for Device Fingerprinting In Multimedia Forensics
http://arxiv.org/abs/2004.10469
AUTHORS:        Yijun Quan ; Chang-Tsun Li ; Yujue Zhou ; Li Li
COMMENTS:       Paper accepted to IEEE International Conference on Multimedia and Expo 2020 (ICME 2020)
HIGHLIGHT:      In this paper, we present the Warwick Image Forensics Dataset, an image dataset of more than 58,600 images captured using 14 digital cameras with various exposure settings.

35, TITLE:      Two-Stream FCNs to Balance Content and Style for Style Transfer
http://arxiv.org/abs/1911.08079
AUTHORS:        Duc Minh Vo ; Akihiro Sugimoto
COMMENTS:       published in Machine Vision and Applications
HIGHLIGHT:      In this paper, we propose an end-to-end two-stream Fully Convolutional Networks (FCNs) aiming at balancing the contributions of the content and the style in rendered images.

36, TITLE:      Recursed is not Recursive: A Jarring Result
http://arxiv.org/abs/2002.05131
AUTHORS:        Erik Demaine ; Justin Kopinsky ; Jayson Lynch
COMMENTS:       Submitted to MFCS2020, 21 pages
HIGHLIGHT:      We prove that Recursed is RE-complete and thus undecidable (not recursive) by a reduction from the Post Correspondence Problem.

37, TITLE:      Establishing the Quantum Supremacy Frontier with a 281 Pflop/s Simulation
http://arxiv.org/abs/1905.00444
AUTHORS:        Benjamin Villalonga ; Dmitry Lyakh ; Sergio Boixo ; Hartmut Neven ; Travis S. Humble ; Rupak Biswas ; Eleanor G. Rieffel ; Alan Ho ; Salvatore Mandrà
COMMENTS:       The paper has been published in Quantum Science and Technology
HIGHLIGHT:      We report HPC simulations of hard random quantum circuits (RQC), which have been recently used as a benchmark for the first experimental demonstration of Quantum Supremacy, sustaining an average performance of 281 Pflop/s (true single precision) on Summit, currently the fastest supercomputer in the World. In addition, we propose a standard benchmark for NISQ computers based on qFlex.

38, TITLE:      Deep transfer learning for improving single-EEG arousal detection
http://arxiv.org/abs/2004.05111
AUTHORS:        Alexander Neergaard Olesen ; Poul Jennum ; Emmanuel Mignot ; Helge B. D. Sorensen
COMMENTS:       Accepted for presentation at EMBC2020
HIGHLIGHT:      Specifically, we train a baseline model on multivariate polysomnography data and subsequently replace the first two layers to prepare the architecture for single-channel electroencephalography data.

39, TITLE:      Multi-Resolution POMDP Planning for Multi-Object Search in 3D
http://arxiv.org/abs/2005.02878
AUTHORS:        Kaiyu Zheng ; Yoonchang Sung ; George Konidaris ; Stefanie Tellex
COMMENTS:       13 pages, 5 figures, 4 tables
HIGHLIGHT:      We propose a new approach that enables the robot to efficiently search for objects in 3D, taking occlusions into account.

40, TITLE:      Hierarchical Neural Architecture Search for Single Image Super-Resolution
http://arxiv.org/abs/2003.04619
AUTHORS:        Yong Guo ; Yongsheng Luo ; Zhenhao He ; Jin Huang ; Jian Chen
HIGHLIGHT:      To address the above issues, we propose a Hierarchical Neural Architecture Search (HNAS) method to automatically design promising architectures with different requirements of computation cost.

41, TITLE:      Maximal Closed Set and Half-Space Separations in Finite Closure Systems
http://arxiv.org/abs/2001.04417
AUTHORS:        Florian Seiffarth ; Tamas Horvath ; Stefan Wrobel
COMMENTS:       An early version of this paper was presented at ECML/PKDD 2019 and has appeared in the Lecture Notes in Computer Science, Machine Learning and Knowledge Discovery in Databases - European Conference, ECML PKDD 2019
HIGHLIGHT:      As a first approach to overcome this negative result, we relax the problem to maximal closed set separation, give a greedy algorithm solving this problem with a linear number of closure operator calls, and show that this bound is sharp.

42, TITLE:      The Sensitivity of Language Models and Humans to Winograd Schema Perturbations
http://arxiv.org/abs/2005.01348
AUTHORS:        Mostafa Abdou ; Vinit Ravishankar ; Maria Barrett ; Yonatan Belinkov ; Desmond Elliott ; Anders Søgaard
COMMENTS:       ACL 2020
HIGHLIGHT:      Our results highlight interesting differences between humans and language models: language models are more sensitive to number or gender alternations and synonym replacements than humans, and humans are more stable and consistent in their predictions, maintain a much higher absolute performance, and perform better on non-associative instances than associative ones.

43, TITLE:      The Mapillary Traffic Sign Dataset for Detection and Classification on a Global Scale
http://arxiv.org/abs/1909.04422
AUTHORS:        Christian Ertler ; Jerneja Mislej ; Tobias Ollmann ; Lorenzo Porzi ; Gerhard Neuhold ; Yubin Kuang
COMMENTS:       17 pages, 15 figures
HIGHLIGHT:      In this paper, we introduce a traffic sign benchmark dataset of 100K street-level images around the world that encapsulates diverse scenes, wide coverage of geographical locations, and varying weather and lighting conditions and covers more than 300 manually annotated traffic sign classes.

44, TITLE:      Personal Health Knowledge Graphs for Patients
http://arxiv.org/abs/2004.00071
AUTHORS:        Nidhi Rastogi ; Mohammed J. Zaki
COMMENTS:       3 pages, workshop paper
HIGHLIGHT:      Personal Health Knowledge Graphs for Patients

45, TITLE:      Temporal Event Segmentation using Attention-based Perceptual Prediction Model for Continual Learning
http://arxiv.org/abs/2005.02463
AUTHORS:        Ramy Mounir ; Roman Gula ; Jörn Theuerkauf ; Sudeep Sarkar
HIGHLIGHT:      In this work, we present a continual learning perceptual prediction framework (influenced by cognitive psychology) capable of temporal event segmentation through understanding of the underlying representation of objects within individual frames.

46, TITLE:      On the list recoverability of randomly punctured codes
http://arxiv.org/abs/2005.02478
AUTHORS:        Ben Lund ; Aditya Potukuchi
COMMENTS:       11 pages
HIGHLIGHT:      We show that a random puncturing of a code with good distance is list recoverable beyond the Johnson bound.

47, TITLE:      Watching the World Go By: Representation Learning from Unlabeled Videos
http://arxiv.org/abs/2003.07990
AUTHORS:        Daniel Gordon ; Kiana Ehsani ; Dieter Fox ; Ali Farhadi
HIGHLIGHT:      In this paper, we argue that videos offer this natural augmentation for free.

48, TITLE:      Teaching Cameras to Feel: Estimating Tactile Physical Properties of Surfaces From Images
http://arxiv.org/abs/2004.14487
AUTHORS:        Matthew Purri ; Kristin Dana
COMMENTS:       19 pages, 5 figures, 6 tables
HIGHLIGHT:      In this work, we introduce the challenging task of estimating a set of tactile physical properties from visual information.

49, TITLE:      Provenance for the Description Logic ELHr
http://arxiv.org/abs/2001.07541
AUTHORS:        Camille Bourgaux ; Ana Ozaki ; Rafael Peñaloza ; Livia Predoiu
COMMENTS:       This is the long version of IJCAI 2020 paper 2243 (24 pages)
HIGHLIGHT:      We address the problem of handling provenance information in ELHr ontologies.

50, TITLE:      Intra-Variable Handwriting Inspection Reinforced with Idiosyncrasy Analysis
http://arxiv.org/abs/1912.12168
AUTHORS:        Chandranath Adak ; Bidyut B. Chaudhuri ; Chin-Teng Lin ; Michael Blumenstein
HIGHLIGHT:      In this paper, we work on intra-variable handwriting, where the writing samples of an individual can vary significantly.