2020.05.26.txt

==========New Papers==========
1, TITLE:       Rethinking of Pedestrian Attribute Recognition: Realistic Datasets with Efficient Method
http://arxiv.org/abs/2005.11909
AUTHORS:        Jian Jia ; Houjing Huang ; Wenjie Yang ; Xiaotang Chen ; Kaiqi Huang
COMMENTS:       12 pages, 4 figures
HIGHLIGHT:      To address this problem, we propose two realistic datasets PETA\textsubscript{$zs$} and RAPv2\textsubscript{$zs$} following zero-shot setting of pedestrian identities based on PETA and RAPv2 datasets.

2, TITLE:       Adaptive Adversarial Logits Pairing
http://arxiv.org/abs/2005.11904
AUTHORS:        Shangxi Wu ; Jitao Sang ; Kaiyuan Xu ; Guanhua Zheng ; Changsheng Xu
HIGHLIGHT:      Based on the analysis of state-of-the-art defense solution Adversarial Logits Pairing (ALP), we observed in this work that: (1) The inference of adversarially robust models tends to rely on fewer high-contribution features compared with vulnerable ones.

3, TITLE:       mr2NST: Multi-Resolution and Multi-Reference Neural Style Transfer for Mammography
http://arxiv.org/abs/2005.11926
AUTHORS:        Sheng Wang ; Jiayu Huo ; Xi Ouyang ; Jifei Che ; Xuhua Ren ; Zhong Xue ; Qian Wang ; Jie-Zhi Cheng
HIGHLIGHT:      In this study, we explicitly address style variety issue with the proposed multi-resolution and multi-reference neural style transfer (mr2NST) network.

4, TITLE:       Visual Localization Using Semantic Segmentation and Depth Prediction
http://arxiv.org/abs/2005.11922
AUTHORS:        Huanhuan Fan ; Yuhao Zhou ; Ang Li ; Shuang Gao ; Jijunnan Li ; Yandong Guo
HIGHLIGHT:      In this paper, we propose a monocular visual localization pipeline leveraging semantic and depth cues.

5, TITLE:       A Bayesian-inspired, deep learning, semi-supervised domain adaptation technique for land cover mapping
http://arxiv.org/abs/2005.11930
AUTHORS:        Benjamin Lucas ; Charlotte Pelletier ; Daniel Schmidt ; Geoffrey I. Webb ; François Petitjean
HIGHLIGHT:      In this paper we present Sourcerer, a Bayesian-inspired, deep learning-based, semi-supervised DA technique for producing land cover maps from SITS data.

6, TITLE:       Multi-Margin based Decorrelation Learning for Heterogeneous Face Recognition
http://arxiv.org/abs/2005.11945
AUTHORS:        Bing Cao ; Nannan Wang ; Xinbo Gao ; Jie Li ; Zhifeng Li
COMMENTS:       IJCAI 2019
HIGHLIGHT:      This paper presents a deep neural network approach namely Multi-Margin based Decorrelation Learning (MMDL) to extract decorrelation representations in a hyperspherical space for cross-domain face images.

7, TITLE:       Interlayer and Intralayer Scale Aggregation for Scale-invariant Crowd Counting
http://arxiv.org/abs/2005.11943
AUTHORS:        Mingjie Wang ; Hao Cai ; Jun Zhou ; Minglun Gong
HIGHLIGHT:      To overcome these limitations, a Single-column Scale-invariant Network (ScSiNet) is presented in this paper, which extracts sophisticated scale-invariant features via the combination of interlayer multi-scale integration and a novel intralayer scale-invariant transformation (SiT).

8, TITLE:       An End-to-End Mispronunciation Detection System for L2 English Speech Leveraging Novel Anti-Phone Modeling
http://arxiv.org/abs/2005.11950
AUTHORS:        Bi-Cheng Yan ; Meng-Che Wu ; Hsiao-Tsung Hung ; Berlin Chen
COMMENTS:       5 pages, 2 figures, Submitted to INTERSPEECH 2020
HIGHLIGHT:      In view of this, we propose to conduct MDD with a novel end- to-end automatic speech recognition (E2E-based ASR) approach.

9, TITLE:       Non-Destructive Sample Generation From Conditional Belief Functions
http://arxiv.org/abs/2005.11963
AUTHORS:        Mieczysław A. Kłopotek
HIGHLIGHT:      This paper presents a new approach to generate samples from conditional belief functions for a restricted but non trivial subset of conditional belief functions.

10, TITLE:      Keypoints Localization for Joint Vertebra Detection and Fracture Severity Quantification
http://arxiv.org/abs/2005.11960
AUTHORS:        Maxim Pisov ; Vladimir Kondratenko ; Alexey Zakharov ; Alexey Petraikin ; Victor Gombolevskiy ; Sergey Morozov ; Mikhail Belyaev
COMMENTS:       Accepted to MICCAI-2020
HIGHLIGHT:      We propose a new two-step algorithm to localize the vertebral column in 3D CT images and then to simultaneously detect individual vertebrae and quantify fractures in 2D.

11, TITLE:      On Irrelevance of Attributes in Flexible Prediction
http://arxiv.org/abs/2005.11979
AUTHORS:        Mieczyslaw A. Klopotek ; Andrzej Matuszewski
HIGHLIGHT:      On Irrelevance of Attributes in Flexible Prediction

12, TITLE:      Hyperspectral Image Classification with Attention Aided CNNs
http://arxiv.org/abs/2005.11977
AUTHORS:        Renlong Hang ; Zhu Li ; Qingshan Liu ; Pedram Ghamisi ; Shuvra S. Bhattacharyya
HIGHLIGHT:      Along this direction, we propose an attention aided CNN model for spectral-spatial classification of hyperspectral images.

13, TITLE:      Deep Learning Models for Automatic Summarization
http://arxiv.org/abs/2005.11988
AUTHORS:        Pirmin Lemberger
COMMENTS:       13 pages, 5 figures
HIGHLIGHT:      We will discuss in particular applications of pointer networks, hierarchical Transformers and Reinforcement Learning.

14, TITLE:      Pointwise Paraphrase Appraisal is Potentially Problematic
http://arxiv.org/abs/2005.11996
AUTHORS:        Hannah Chen ; Yangfeng Ji ; David Evans
HIGHLIGHT:      This pointwise-based evaluation method does not match well the objective of most real world applications, so the goal of our work is to understand how models which perform well under pointwise evaluation may fail in practice and find better methods for evaluating paraphrase identification models.

15, TITLE:      Eye Gaze Controlled Robotic Arm for Persons with SSMI
http://arxiv.org/abs/2005.11994
AUTHORS:        Vinay Krishna Sharma ; L. R. D. Murthy ; KamalPreet Singh Saluja ; Vimal Mollyn ; Gourav Sharma ; Pradipta Biswas
COMMENTS:       Citation: VK Sharma, KPS Saluja, LRD Murthy, G Sharma and P Biswas, Webcam Controlled Robotic Arm for Persons with SSMI, Technology and Disability 32 (3), IOS Press 2020 [Official journal of EU AAATE association]
HIGHLIGHT:      Objective: We aimed to automate the eye tracking process electronically by using commercially available tablet, computer or laptop and without requiring any dedicated hardware for eye gaze tracking.

16, TITLE:      Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI
http://arxiv.org/abs/2005.11524
AUTHORS:        Anas Tahir ; Yazan Qiblawey ; Amith Khandakar ; Tawsifur Rahman ; Uzair Khurshid ; Farayi Musharavati ; Serkan Kiranyaz ; Muhammad E. H. Chowdhury
COMMENTS:       10 Figures, 4 Tables
HIGHLIGHT:      In this work, authors used deep machine learning algorithms along with innovative image pre-processing techniques to distinguish COVID-19 images from SARS and MERS images.

17, TITLE:      Transformer-based Context-aware Sarcasm Detection in Conversation Threads from Social Media
http://arxiv.org/abs/2005.11424
AUTHORS:        Xiangjue Dong ; Changmao Li ; Jinho D. Choi
COMMENTS:       To be published in ACL FigLang2020
HIGHLIGHT:      We present a transformer-based sarcasm detection model that accounts for the context from the entire conversation thread for more robust predictions.

18, TITLE:      Hashing-based Non-Maximum Suppression for Crowded Object Detection
http://arxiv.org/abs/2005.11426
AUTHORS:        Jianfeng Wang ; Xi Yin ; Lijuan Wang ; Lei Zhang
HIGHLIGHT:      In this paper, we propose an algorithm, named hashing-based non-maximum suppression (HNMS) to efficiently suppress the non-maximum boxes for object detection.

19, TITLE:      Multi-view polarimetric scattering cloud tomography and retrieval of droplet size
http://arxiv.org/abs/2005.11423
AUTHORS:        Aviad Levis ; Yoav Y. Schechner ; Anthony B. Davis ; Jesse Loveridge
HIGHLIGHT:      This work defines and derives a framework for a full 3D tomography of cloud droplets for both their mass concentration in space and their distribution across a range of sizes.

20, TITLE:      S3VAE: Self-Supervised Sequential VAE for Representation Disentanglement and Data Generation
http://arxiv.org/abs/2005.11437
AUTHORS:        Yizhe Zhu ; Martin Renqiang Min ; Asim Kadav ; Hans Peter Graf
COMMENTS:       to appear in CVPR2020
HIGHLIGHT:      We propose a sequential variational autoencoder to learn disentangled representations of sequential data (e.g., videos and audios) under self-supervision.

21, TITLE:      Fine-Grain Few-Shot Vision via Domain Knowledge as Hyperspherical Priors
http://arxiv.org/abs/2005.11450
AUTHORS:        Bijan Haney ; Alexander Lavin
HIGHLIGHT:      We describe how to construct a hypersphere of prototypes that embed a-priori domain information, and demonstrate the effectiveness of the approach on challenging benchmark datasets for fine-grain classification, with top results for one-shot classification and 5x speedups in training time.

22, TITLE:      Designing with Static Capabilities and Effects: Use, Mention, and Invariants
http://arxiv.org/abs/2005.11444
AUTHORS:        Colin S. Gordon
COMMENTS:       Preprint of ECOOP 2020 paper
HIGHLIGHT:      Along the way, we highlight how seemingly-minor aspects of type systems -- weakening/framing and the mere existence of type contexts -- play a subtle role in the efficacy of these systems.

23, TITLE:      COVID-19 Public Opinion and Emotion Monitoring System Based on Time Series Thermal New Word Mining
http://arxiv.org/abs/2005.11458
AUTHORS:        Yixian Zhang ; Jieren Chen ; Boyi Liu ; Yifan Yang ; Haocheng Li ; Xinyi Zheng ; Xi Chen ; Tenglong Ren ; Naixue Xiong
HIGHLIGHT:      We designed and implemented the COVID-19 public opinion monitoring system based on time series thermal new word mining.

24, TITLE:      Delving into the Imbalance of Positive Proposals in Two-stage Object Detection
http://arxiv.org/abs/2005.11472
AUTHORS:        Zheng Ge ; Zequn Jie ; Xin Huang ; Chengzheng Li ; Osamu Yoshie
HIGHLIGHT:      In this work, we observe two crucial yet never discussed imbalance issues.

25, TITLE:      Learning from Naturalistic Driving Data for Human-like Autonomous Highway Driving
http://arxiv.org/abs/2005.11470
AUTHORS:        Donghao Xu ; Zhezhang Ding ; Xu He ; Huijing Zhao ; Mathieu Moze ; François Aioun ; Franck Guillemard
COMMENTS:       14 pages, 9 figures. Submitted to T.ITS
HIGHLIGHT:      In this study, a method of learning cost parameters of a motion planner from naturalistic driving data is proposed.

26, TITLE:      Misalignment Resilient Diffractive Optical Networks
http://arxiv.org/abs/2005.11464
AUTHORS:        Deniz Mengu ; Yifan Zhao ; Nezih T. Yardimci ; Yair Rivenson ; Mona Jarrahi ; Aydogan Ozcan
COMMENTS:       15 Pages, 6 Figures
HIGHLIGHT:      Here, we introduce and experimentally demonstrate a new training scheme that significantly increases the robustness of diffractive networks against 3D misalignments and fabrication tolerances in the physical implementation of a trained diffractive network.

27, TITLE:      Attention-guided Context Feature Pyramid Network for Object Detection
http://arxiv.org/abs/2005.11475
AUTHORS:        Junxu Cao ; Qi Chen ; Jun Guo ; Ruichao Shi
HIGHLIGHT:      In this paper, to tackle this issue, we build a novel architecture, called Attention-guided Context Feature Pyramid Network (AC-FPN), that exploits discriminative information from various large receptive fields via integrating attention-guided multi-path features.

28, TITLE:      From Witch's Shot to Music Making Bones -- Resources for Medical Laymen to Technical Language and Vice Versa
http://arxiv.org/abs/2005.11494
AUTHORS:        Laura Seiffe ; Oliver Marten ; Michael Mikhailov ; Sven Schmeier ; Sebastian Möller ; Roland Roller
COMMENTS:       In Proceedings of LREC 2020
HIGHLIGHT:      This work presents baseline data sources in order to address this challenge for German. We introduce a new data set which annotates medical laymen and technical expressions in a patient forum, along with a set of medical synonyms and definitions, and present first baseline results on the data.

29, TITLE:      Summarizing and Exploring Tabular Data in Conversational Search
http://arxiv.org/abs/2005.11490
AUTHORS:        Shuo Zhang ; Zhuyun Dai ; Krisztian Balog ; Jamie Callan
COMMENTS:       Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR 2020), 2020
HIGHLIGHT:      We propose to generate natural language summaries as answers to describe the complex information contained in a table. Through crowdsourcing experiments, we build a new conversation-oriented, open-domain table summarization dataset.

30, TITLE:      Self-Training for Domain Adaptive Scene Text Detection
http://arxiv.org/abs/2005.11487
AUTHORS:        Yudi Chen ; Wei Wang ; Yu Zhou ; Fei Yang ; Dongbao Yang ; Weiping Wang
HIGHLIGHT:      To address this problem, we propose a self-training framework to automatically mine hard examples with pseudo-labels from unannotated videos or images.

31, TITLE:      AnimGAN: A Spatiotemporally-Conditioned Generative Adversarial Network for Character Animation
http://arxiv.org/abs/2005.11489
AUTHORS:        Maryam Sadat Mirzaei ; Kourosh Meshgi ; Etienne Frigo ; Toyoaki Nishida
COMMENTS:       Submitted to ICIP 2020
HIGHLIGHT:      We proposed a spatiotemporally-conditioned GAN that generates a sequence that is similar to a given sequence in terms of semantics and spatiotemporal dynamics.

32, TITLE:      Stable Style Transformer: Delete and Generate Approach with Encoder-Decoder for Text Style Transfer
http://arxiv.org/abs/2005.12086
AUTHORS:        Joosung Lee
COMMENTS:       9 pages, 3 figures
HIGHLIGHT:      In this work, we introduce a method that follows two stages in non-parallel datasets.

33, TITLE:      Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
http://arxiv.org/abs/2005.12094
AUTHORS:        Daniel Hershcovich ; Miryam de Lhoneux ; Artur Kulmizev ; Elham Pejhan ; Joakim Nivre
COMMENTS:       IWPT shared task 2020
HIGHLIGHT:      We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.

34, TITLE:      Knee Point Identification Based on Trade-Off Utility
http://arxiv.org/abs/2005.11600
AUTHORS:        Ke Li ; Haifeng Nie ; Huifu Gao ; Xin Yao
HIGHLIGHT:      In this paper, we propose a simple and effective knee point identification method based on trade-off utility, dubbed KPITU, to help decision makers identify knee points from a given set of trade-off solutions.

35, TITLE:      Geometric algorithms for predicting resilience and recovering damage in neural networks
http://arxiv.org/abs/2005.11603
AUTHORS:        Guruprasad Raghavan ; Jiayi Li ; Matt Thomson
COMMENTS:       10 pages and 4 figures
HIGHLIGHT:      In this paper, we establish a mathematical framework to analyze the resilience of artificial neural networks through the lens of differential geometry.

36, TITLE:      Bayesian Neural Networks at Scale: A Performance Analysis and Pruning Study
http://arxiv.org/abs/2005.11619
AUTHORS:        Himanshu Sharma ; Elise Jennings
HIGHLIGHT:      We present a performance and scalability comparison of training the VGG-16 and Resnet-18 models on a Cray-XC40 cluster.

37, TITLE:      One-Shot Unsupervised Cross-Domain Detection
http://arxiv.org/abs/2005.11610
AUTHORS:        Antonio D'Innocente ; Francesco Cappio Borlino ; Silvia Bucci ; Barbara Caputo ; Tatiana Tommasi
HIGHLIGHT:      This paper addresses this setting, presenting an object detection algorithm able to perform unsupervised adaption across domains by using only one target sample, seen at test time.

38, TITLE:      ShapeAdv: Generating Shape-Aware Adversarial 3D Point Clouds
http://arxiv.org/abs/2005.11626
AUTHORS:        Kibok Lee ; Zhuoyuan Chen ; Xinchen Yan ; Raquel Urtasun ; Ersin Yumer
COMMENTS:       3D Point Clouds, Adversarial Learning
HIGHLIGHT:      We introduce ShapeAdv, a novel framework to study shape-aware adversarial perturbations that reflect the underlying shape variations (e.g., geometric deformations and structural differences) in the 3D point cloud space.

39, TITLE:      Unsupervised Geometric Disentanglement for Surfaces via CFAN-VAE
http://arxiv.org/abs/2005.11622
AUTHORS:        N. Joseph Tatro ; Stefan C. Schonsheck ; Rongjie Lai
HIGHLIGHT:      This work introduces a novel mesh feature, the conformal factor and normal feature (CFAN), for use in mesh convolutional autoencoders.

40, TITLE:      RAPiD: Rotation-Aware People Detection in Overhead Fisheye Images
http://arxiv.org/abs/2005.11623
AUTHORS:        Zhihao Duan ; M. Ozan Tezcan ; Hayato Nakamura ; Prakash Ishwar ; Janusz Konrad
COMMENTS:       CVPR 2020 OmniCV Workshop paper extended version
HIGHLIGHT:      In this work, we develop an end-to-end rotation-aware people detection method, named RAPiD, that detects people using arbitrarily-oriented bounding boxes. We have also created a new dataset with spatio-temporal annotations of rotated bounding boxes, for people detection as well as other vision tasks in overhead fisheye videos.

41, TITLE:      Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
http://arxiv.org/abs/2005.11633
AUTHORS:        Bojian Yin ; Federico Corradi ; Sander M. Bohté
COMMENTS:       11 pages,5 figures
HIGHLIGHT:      From this, we calculate a $>$100x energy improvement for our SRNNs over classical RNNs on the harder tasks.

42, TITLE:      PoliteCamera: Respecting Strangers' Privacy in Mobile Photographing
http://arxiv.org/abs/2005.11634
AUTHORS:        Ang Li ; Wei Du ; Qinghua Li
HIGHLIGHT:      In this paper, we propose a cooperative mobile photographing scheme called PoliteCamera to protect strangers' privacy.

43, TITLE:      MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
http://arxiv.org/abs/2005.11630
AUTHORS:        Ang Li ; Chunpeng Wu ; Yiran Chen ; Bin Ni
HIGHLIGHT:      To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones.

44, TITLE:      miniKanren as a Tool for Symbolic Computation in Python
http://arxiv.org/abs/2005.11644
AUTHORS:        Brandon T. Willard
HIGHLIGHT:      In this article, we give a brief overview of the current state and future potential of symbolic computation within the Python statistical modeling and machine learning community.

45, TITLE:      Master-Auxiliary: an efficient aggregation strategy for video anomaly detection
http://arxiv.org/abs/2005.11645
AUTHORS:        Zhiguo Wang ; Zhongliang Yang ; Yujin Zhang
COMMENTS:       8 pages
HIGHLIGHT:      This paper proposes an efficient strategy to aggregate multiple detectors together.

46, TITLE:      Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
http://arxiv.org/abs/2005.11640
AUTHORS:        Chen Liu ; Su Zhu ; Zijian Zhao ; Ruisheng Cao ; Lu Chen ; Kai Yu
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly.

47, TITLE:      Robust Object Detection under Occlusion with \\Context-Aware CompositionalNets
http://arxiv.org/abs/2005.11643
AUTHORS:        Angtian Wang ; Yihong Sun ; Adam Kortylewski ; Alan Yuille
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this work, we propose to overcome two limitations of CompositionalNets which will enable them to detect partially occluded objects: 1) CompositionalNets, as well as other DCNN architectures, do not explicitly separate the representation of the context from the object itself.

48, TITLE:      A Note on the Concrete Hardness of the Shortest Independent Vectors Problem in Lattices
http://arxiv.org/abs/2005.11654
AUTHORS:        Divesh Aggarwal ; Eldon Chung
HIGHLIGHT:      In order to formally define this requirement on the $\mathsf{CVP}$ instance, we introduce a new computational problem called the Gap Closest Vector Problem with Bounded Minima.

49, TITLE:      Self-supervised Robust Object Detectors from Partially Labelled datasets
http://arxiv.org/abs/2005.11549
AUTHORS:        Mahdieh Abbasi ; Denis Laurendeau ; Christian Gagne
HIGHLIGHT:      With the goal of training \emph{one integrated robust object detector with high generalization performance}, we propose a training framework to overcome missing-label challenge of the merged datasets.

50, TITLE:      ProAlignNet : Unsupervised Learning for Progressively Aligning Noisy Contours
http://arxiv.org/abs/2005.11546
AUTHORS:        VSR Veeravasarapu ; Abhishek Goel ; Deepak Mittal ; Maneesh Singh
COMMENTS:       Accepted at CVPR 2020
HIGHLIGHT:      This work presents a novel ConvNet, "ProAlignNet" that accounts for large scale misalignments and complex transformations between the contour shapes.

51, TITLE:      Finding Small Satisfying Assignments Faster Than Brute Force: A Fine-grained Perspective into Boolean Constraint Satisfaction
http://arxiv.org/abs/2005.11541
AUTHORS:        Marvin Künnemann ; Dániel Marx
HIGHLIGHT:      More precisely, we aim to determine, for any finite constraint family, the optimal running time $f(k)n^{g(k)}$ required to find satisfying assignments that set precisely $k$ of the $n$ variables to $1$.

52, TITLE:      Invariant 3D Shape Recognition using Predictive Modular Neural Networks
http://arxiv.org/abs/2005.11558
AUTHORS:        Vasileios Petridis
COMMENTS:       17 pages, 2 figures
HIGHLIGHT:      The methods presented in this paper can be applied to many problems such as gesture recognition, action recognition, dynamic texture recognition etc.

53, TITLE:      Underwater object detection using Invert Multi-Class Adaboost with deep learning
http://arxiv.org/abs/2005.11552
AUTHORS:        Long Chen ; Zhihua Liu ; Lei Tong ; Zheheng Jiang ; Shengke Wang ; Junyu Dong ; Huiyu Zhou
HIGHLIGHT:      In recent years, deep learning based methods have achieved promising performance in standard object detection.

54, TITLE:      Evolution of Cooperative Hunting in Artificial Multi-layered Societies
http://arxiv.org/abs/2005.11580
AUTHORS:        Honglin Bao ; Wolfgang Banzhaf
COMMENTS:       29 pages, 6 figures, an extension of our ALife 2018 conference paper
HIGHLIGHT:      In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society.

55, TITLE:      Hierarchical Feature Embedding for Attribute Recognition
http://arxiv.org/abs/2005.11576
AUTHORS:        Jie Yang ; Jiarou Fan ; Yiru Wang ; Yige Wang ; Weihao Gan ; Lin Liu ; Wei Wu
COMMENTS:       CVPR 2020
HIGHLIGHT:      To address this problem, we propose a hierarchical feature embedding (HFE) framework, which learns a fine-grained feature embedding by combining attribute and ID information.

56, TITLE:      Revisiting Street-to-Aerial View Image Geo-localization and Orientation Estimation
http://arxiv.org/abs/2005.11592
AUTHORS:        Sijie Zhu ; Taojiannan Yang ; Chen Chen
HIGHLIGHT:      In this paper, we revisit this problem and point out the ignored issue about image alignment information.

57, TITLE:      MaxSAT Resolution and Subcube Sums
http://arxiv.org/abs/2005.11589
AUTHORS:        Yuval Filmus ; Meena Mahajan ; Gaurav Sood ; Marc Vinyals
HIGHLIGHT:      We study the MaxRes rule in the context of certifying unsatisfiability.

58, TITLE:      A Preliminary Study for Identification of Additive Manufactured Objects with Transmitted Images
http://arxiv.org/abs/2005.12027
AUTHORS:        Kenta Yamamoto ; Ryota Kawamura ; Kazuki Takazawa ; Hiroyuki Osone ; Yoichi Ochiai
HIGHLIGHT:      In this study, we have developed a product identification system that does not require embedding barcodes inside.

59, TITLE:      A Joint Pixel and Feature Alignment Framework for Cross-dataset Palmprint Recognition
http://arxiv.org/abs/2005.12044
AUTHORS:        Huikai Shao ; Dexing Zhong
COMMENTS:       12 pages, 7 figures
HIGHLIGHT:      Therefore, we propose a novel Joint Pixel and Feature Alignment (JPFA) framework for such cross-dataset palmprint recognition scenarios.

60, TITLE:      Knowledge Graph Simple Question Answering for Unseen Domains
http://arxiv.org/abs/2005.12040
AUTHORS:        Georgios Sidiropoulos ; Nikos Voskarides ; Evangelos Kanoulas
COMMENTS:       Accepted at AKBC 2020
HIGHLIGHT:      We propose a data-centric domain adaptation framework that consists of a KGSQA system that is applicable to new domains, and a sequence to sequence question generation method that automatically generates question-answer pairs for the new domain.

61, TITLE:      An interpretable automated detection system for FISH-based HER2 oncogene amplification testing in histo-pathological routine images of breast and gastric cancer diagnostics
http://arxiv.org/abs/2005.12066
AUTHORS:        Sarah Schmell ; Falk Zakrzewski ; Walter de Back ; Martin Weigert ; Uwe Schmidt ; Torsten Wenke ; Silke Zeugner ; Robert Mantey ; Christian Sperling ; Ingo Roeder ; Pia Hoenscheid ; Daniela Aust ; Gustavo Baretton
HIGHLIGHT:      Here, we develop an interpretable, deep learning (DL)-based pipeline which automates the evaluation of FISH images with respect to HER2 gene amplification testing.

62, TITLE:      Combinatorics of a Discrete Trajectory Space for Robot Motion Planning
http://arxiv.org/abs/2005.12064
AUTHORS:        Felix Wiebe ; Shivesh Kumar ; Daniel Harnack ; Malte Langosz ; Hendrik Wöhrle ; Frank Kirchner
COMMENTS:       8 pages, 3 figures, to be published in the proceedings of 2nd IMA Conference on Mathematics of Robotics 2021
HIGHLIGHT:      Using lattice path methods, we provide estimates for the complexity of motion planning by counting the number of possible trajectories in a discrete robot configuration space.

63, TITLE:      Happy Are Those Who Grade without Seeing: A Multi-Task Learning Approach to Grade Essays Using Gaze Behaviour
http://arxiv.org/abs/2005.12078
AUTHORS:        Sandeep Mathias ; Rudra Murthy ; Diptesh Kanojia ; Abhijit Mishra ; Pushpak Bhattacharyya
HIGHLIGHT:      In this paper, we propose a way to improve automatic essay grading using gaze behaviour, where the gaze features are learnt at run time using a multi-task learning framework.

64, TITLE:      Visual Attention: Deep Rare Features
http://arxiv.org/abs/2005.12073
AUTHORS:        Mancas Matei ; Kong Phutphalla ; Gosselin Bernard
COMMENTS:       6 pages, double-colmun, accepted to IVPR2020
HIGHLIGHT:      In this paper, we propose a model called DeepRare2019 (DR) which uses the power of DNNs feature extraction and the genericity of feature-engineered algorithms.

65, TITLE:      Egocentric Human Segmentation for Mixed Reality
http://arxiv.org/abs/2005.12074
AUTHORS:        Andrija Gajic ; Ester Gonzalez-Sosa ; Diego Gonzalez-Morin ; Marcos Escudero-Viñolo ; Alvaro Villegas
COMMENTS:       Accepted for presentation at EPIC@CVPR2020 workshop
HIGHLIGHT:      The objective of this work is to segment human body parts from egocentric video using semantic segmentation networks. Our contribution is two-fold: i) we create a semi-synthetic dataset composed of more than 15, 000 realistic images and associated pixel-wise labels of egocentric human body parts, such as arms or legs including different demographic factors; ii) building upon the ThunderNet architecture, we implement a deep learning semantic segmentation algorithm that is able to perform beyond real-time requirements (16 ms for 720 x 720 images).

66, TITLE:      Policy Entropy for Out-of-Distribution Classification
http://arxiv.org/abs/2005.12069
AUTHORS:        Andreas Sedlmeier ; Robert Müller ; Steffen Illium ; Claudia Linnhoff-Popien
HIGHLIGHT:      In this work, we propose PEOC, a new policy entropy based out-of-distribution classifier that reliably detects unencountered states in deep reinforcement learning.

67, TITLE:      A Novel Distributed Representation of News (DRNews) for Stock Market Predictions
http://arxiv.org/abs/2005.11706
AUTHORS:        Ye Ma ; Lu Zong ; Peiwan Wang
COMMENTS:       25 pages
HIGHLIGHT:      In this study, a novel Distributed Representation of News (DRNews) model is developed and applied in deep learning-based stock market predictions.

68, TITLE:      When does MAML Work the Best? An Empirical Study on Model-Agnostic Meta-Learning in NLP Applications
http://arxiv.org/abs/2005.11700
AUTHORS:        Zequn Liu ; Ruiyi Zhang ; Yiping Song ; Ming Zhang
HIGHLIGHT:      In this paper, we conduct an empirical study to investigate these impacting factors and conclude when MAML works the best based on the experimental results.

69, TITLE:      A Lightweight CNN and Joint Shape-Joint Space (JS2) Descriptor for Radiological Osteoarthritis Detection
http://arxiv.org/abs/2005.11715
AUTHORS:        Neslihan Bayramoglu ; Miika T. Nieminen ; Simo Saarakkala
COMMENTS:       MIUA2020
HIGHLIGHT:      In this study, we propose a fully automated novel method, based on combination of joint shape and convolutional neural network (CNN) based bone texture features, to distinguish between the knee radiographs with and without radiographic osteoarthritis.

70, TITLE:      Multi-view Alignment and Generation in CCA via Consistent Latent Encoding
http://arxiv.org/abs/2005.11716
AUTHORS:        Yaxin Shi ; Yuangang Pan ; Donna Xu ; Ivor W. Tsang
COMMENTS:       37 pages, 22 figures
HIGHLIGHT:      Delving into the impairments of inconsistent encodings, we propose to recover correspondence of the multi-view inputs by matching the marginalization of the joint distribution of multi-view random variables under different forms of factorization.

71, TITLE:      Featherweight Go
http://arxiv.org/abs/2005.11710
AUTHORS:        Robert Griesemer ; Raymond Hu ; Wen Kokke ; Julien Lange ; Ian Lance Taylor ; Bernardo Toninho ; Philip Wadler ; Nobuko Yoshida
COMMENTS:       a full version
HIGHLIGHT:      We describe a design for generics in Go inspired by previous work on Featherweight Java by Igarashi, Pierce, and Wadler.

72, TITLE:      Learning Camera Miscalibration Detection
http://arxiv.org/abs/2005.11711
AUTHORS:        Andrei Cramariuc ; Aleksandar Petrov ; Rohit Suri ; Mayank Mittal ; Roland Siegwart ; Cesar Cadena
COMMENTS:       ICRA 2020
HIGHLIGHT:      Our contributions include a proposed miscalibration metric for RGB cameras and a novel semi-synthetic dataset generation pipeline based on this metric.

73, TITLE:      GoChat: Goal-oriented Chatbots with Hierarchical Reinforcement Learning
http://arxiv.org/abs/2005.11729
AUTHORS:        Jianfeng Liu ; Feiyang Pan ; Ling Luo
HIGHLIGHT:      In this paper, we propose Goal-oriented Chatbots (GoChat), a framework for end-to-end training chatbots to maximize the longterm return from offline multi-turn dialogue datasets.

74, TITLE:      Query Resolution for Conversational Search with Limited Supervision
http://arxiv.org/abs/2005.11723
AUTHORS:        Nikos Voskarides ; Dan Li ; Pengjie Ren ; Evangelos Kanoulas ; Maarten de Rijke
COMMENTS:       SIGIR 2020 full conference paper
HIGHLIGHT:      In this work we focus on multi-turn passage retrieval as a crucial component of conversational search.

75, TITLE:      Adversarial NLI for Factual Correctness in Text Summarisation Models
http://arxiv.org/abs/2005.11739
AUTHORS:        Mario Barrantes ; Benedikt Herudek ; Richard Wang
HIGHLIGHT:      We apply the Adversarial NLI dataset to train the NLI model and show that the model has the potential to enhance factual correctness in abstract summarization.

76, TITLE:      Automatic Discovery of Interpretable Planning Strategies
http://arxiv.org/abs/2005.11730
AUTHORS:        Julian Skirzynski ; Frederic Becker ; Falk Lieder
COMMENTS:       Submitted to the Special Issue on Reinforcement Learning for Real Life in Machine Learning Journal. Code available at https://github.com/RationalityEnhancement/InterpretableStrategyDiscovery
HIGHLIGHT:      We conclude that the methods and findings presented in this article are an important step towards leveraging automatic strategy discovery to improve human decision-making.

77, TITLE:      Domain Specific, Semi-Supervised Transfer Learning for Medical Imaging
http://arxiv.org/abs/2005.11746
AUTHORS:        Jitender Singh Virk ; Deepti R. Bathula
COMMENTS:       9 pages 4 figures
HIGHLIGHT:      Consequently, we propose a lightweight architecture that uses mixed asymmetric kernels (MAKNet) to reduce the number of parameters significantly.

78, TITLE:      High-Resolution Image Inpainting with Iterative Confidence Feedback and Guided Upsampling
http://arxiv.org/abs/2005.11742
AUTHORS:        Yu Zeng ; Zhe Lin ; Jimei Yang ; Jianming Zhang ; Eli Shechtman ; Huchuan Lu
HIGHLIGHT:      To address this challenge, we propose an iterative inpainting method with a feedback mechanism. Furthermore, to mimic real object removal scenarios, we collect a large object mask dataset and synthesize more realistic training data that better simulates user inputs.

79, TITLE:      On the impact of treewidth in the computational complexity of freezing dynamics
http://arxiv.org/abs/2005.11758
AUTHORS:        Eric Goles ; Pedro Montealegre ; Martín Ríos-Wilson ; Guillaume Theyssier
HIGHLIGHT:      In this paper we establish how treewidth and maximum degree of the underlying graph are key parameters which influence the overall computational complexity of finite freezing automata networks.

80, TITLE:      Lite Audio-Visual Speech Enhancement
http://arxiv.org/abs/2005.11769
AUTHORS:        Shang-Yi Chuang ; Yu Tsao ; Chen-Chou Lo ; Hsin-Min Wang
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this study, we propose a Lite AVSE (LAVSE) system to address these problems.

81, TITLE:      The Weisfeiler-Leman dimension of distance-hereditary graphs
http://arxiv.org/abs/2005.11766
AUTHORS:        Alexander L. Gavrilyuk ; Roman Nedela ; Ilia Ponomarenko
HIGHLIGHT:      We prove that the ordinary Weisfeiler-Leman algorithm correctly tests the isomorphism of any two graphs if one of them is distance-hereditary; more precisely, the Weisfeiler-Leman dimension of the class of finite distance-hereditary graphs is equal to $2$.

82, TITLE:      KaLM at SemEval-2020 Task 4: Knowledge-aware Language Models for Comprehension And Generation
http://arxiv.org/abs/2005.11768
AUTHORS:        Jiajing Wan ; Xinting Huang
COMMENTS:       6 pages, 1 figure
HIGHLIGHT:      This paper presents our strategies in SemEval 2020 Task 4: Commonsense Validation and Explanation.

83, TITLE:      Deep Convolutional Neural Network-based Bernoulli Heatmap for Head Pose Estimation
http://arxiv.org/abs/2005.11780
AUTHORS:        Zhongxu Hu ; Yang Xing ; Chen Lv ; Peng Hang ; Jie Liu
HIGHLIGHT:      This paper proposes a novel Bernoulli heatmap for head pose estimation from a single RGB image.

84, TITLE:      Acoustic Word Embedding System for Code-Switching Query-by-example Spoken Term Detection
http://arxiv.org/abs/2005.11777
AUTHORS:        Murong Ma ; Haiwei Wu ; Xuyang Wang ; Lin Yang ; Junjie Wang ; Ming Li
HIGHLIGHT:      In this paper, we propose a deep convolutional neural network-based acoustic word embedding system on code-switching query by example spoken term detection.

85, TITLE:      Deep learning approach to describe and classify fungi microscopic images
http://arxiv.org/abs/2005.11772
AUTHORS:        Bartosz Zieliński ; Agnieszka Sroka-Oleksiak ; Dawid Rymarczyk ; Adam Piekarczyk ; Monika Brzychczy-Włoch
HIGHLIGHT:      In this paper, we apply a machine learning approach based on deep neural networks and Fisher Vector (advanced bag-of-words method) to classify microscopic images of various fungi species.

86, TITLE:      Benefits of temporal information for appearance-based gaze estimation
http://arxiv.org/abs/2005.11670
AUTHORS:        Cristina Palmero ; Oleg V. Komogortsev ; Sachin S. Talathi
COMMENTS:       In ACM Symposium on Eye Tracking Research & Applications (ETRA), 2020
HIGHLIGHT:      In this paper, we investigate whether temporal sequences of eye images, captured using a high-resolution, high-frame rate head-mounted virtual reality system, can be leveraged to enhance the accuracy of an end-to-end appearance-based deep-learning model for gaze estimation.

87, TITLE:      Quadratic Sieve Factorization Quantum Algorithm and its Simulation
http://arxiv.org/abs/2005.11668
AUTHORS:        Amandeep Singh Bhatia ; Ajay Kumar
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      In this paper, we have designed a quantum variant of the second fastest classical factorization algorithm named "Quadratic Sieve".

88, TITLE:      A Question Type Driven and Copy Loss Enhanced Frameworkfor Answer-Agnostic Neural Question Generation
http://arxiv.org/abs/2005.11665
AUTHORS:        Xiuyu Wu ; Nan Jiang ; Yunfang Wu
HIGHLIGHT:      In this paper, we propose two new strategies to deal with this task: question type prediction and copy loss mechanism.

89, TITLE:      Glottal source estimation robustness: A comparison of sensitivity of voice source estimation techniques
http://arxiv.org/abs/2005.11682
AUTHORS:        Thomas Drugman ; Thomas Dubuisson ; Alexis Moinet ; Nicolas D'Alessandro ; Thierry Dutoit
HIGHLIGHT:      This paper addresses the problem of estimating the voice source directly from speech waveforms.

90, TITLE:      Networks with pixels embedding: a method to improve noise resistance in images classification
http://arxiv.org/abs/2005.11679
AUTHORS:        Chi-Chun Zhou ; Hai-Long Tu ; Yi Liua ; Fu-Lin Zhang
HIGHLIGHT:      In this work, we provide a noise-resistance network in images classification by introducing a technique of pixels embedding.

91, TITLE:      Transformer VQ-VAE for Unsupervised Unit Discovery and Speech Synthesis: ZeroSpeech 2020 Challenge
http://arxiv.org/abs/2005.11676
AUTHORS:        Andros Tjandra ; Sakriani Sakti ; Satoshi Nakamura
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, we report our submitted system for the ZeroSpeech 2020 challenge on Track 2019.

92, TITLE:      Integrated Node Encoder for Labelled Textual Networks
http://arxiv.org/abs/2005.11694
AUTHORS:        Ye Ma ; Lu Zong
COMMENTS:       7 pages
HIGHLIGHT:      In this study, we design an integrated node encoder (INE) for textual networks which is jointly trained on the structure-based and label-based objectives.

93, TITLE:      Derivation of Symmetric PCA Learning Rules from a Novel Objective Function
http://arxiv.org/abs/2005.11689
AUTHORS:        Ralf Möller
HIGHLIGHT:      Here we introduce an alternative objective function where it is not necessary to introduce fixed weight factors; instead, the alternative objective function uses squared summands.

94, TITLE:      MASK: A flexible framework to facilitate de-identification of clinical texts
http://arxiv.org/abs/2005.11687
AUTHORS:        Nikola Milosevic ; Gangamma Kalappa ; Hesam Dadafarin ; Mahmoud Azimaee ; Goran Nenadic
HIGHLIGHT:      In this paper, we present MASK, a software package that is designed to perform the de-identification task.

95, TITLE:      Physics-based polynomial neural networks for one-short learning of dynamical systems from one or a few samples
http://arxiv.org/abs/2005.11699
AUTHORS:        Andrei Ivanov ; Uwe Iben ; Anna Golovkina
HIGHLIGHT:      The paper describes practical results on real experiments with both a simple pendulum and one of the largest worldwide X-ray source.

96, TITLE:      The efficiency of deep learning algorithms for detecting anatomical reference points on radiological images of the head profile
http://arxiv.org/abs/2005.12110
AUTHORS:        Konstantin Dobratulin ; Andrey Gaidel ; Irina Aupova ; Anna Ivleva ; Aleksandr Kapishnikov ; Pavel Zelter
HIGHLIGHT:      In this article we investigate the efficiency of deep learning algorithms in solving the task of detecting anatomical reference points on radiological images of the head in lateral projection using a fully convolutional neural network and a fully convolutional neural network with an extended architecture for biomedical image segmentation - U-Net.

97, TITLE:      NILE : Natural Language Inference with Faithful Natural Language Explanations
http://arxiv.org/abs/2005.12116
AUTHORS:        Sawan Kumar ; Partha Talukdar
COMMENTS:       13 pages, 3 figures, Accepted to ACL 2020
HIGHLIGHT:      We propose Natural-language Inference over Label-specific Explanations (NILE), a novel NLI method which utilizes auto-generated label-specific NL explanations to produce labels along with its faithful explanation.

98, TITLE:      Degree-Aware Alignment for Entities in Tail
http://arxiv.org/abs/2005.12132
AUTHORS:        Weixin Zeng ; Xiang Zhao ; Wei Wang ; Jiuyang Tang ; Zhen Tan
COMMENTS:       Accepted by SIGIR 2020
HIGHLIGHT:      For pre-alignment, we propose to amplify long-tail entities, which are of relatively weak structural information, with entity name information that is generally available (but overlooked) in the form of concatenated power mean word embeddings.

99, TITLE:      Learning to Simulate Dynamic Environments with GameGAN
http://arxiv.org/abs/2005.12126
AUTHORS:        Seung Wook Kim ; Yuhao Zhou ; Jonah Philion ; Antonio Torralba ; Sanja Fidler
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we aim to learn a simulator by simply watching an agent interact with an environment.

100, TITLE:     An Audio-enriched BERT-based Framework for Spoken Multiple-choice Question Answering
http://arxiv.org/abs/2005.12142
AUTHORS:        Chia-Chih Kuo ; Shang-Bao Luo ; Kuan-Yu Chen
HIGHLIGHT:      Consequently, an audio-enriched BERT-based SMCQA framework is proposed.

101, TITLE:     Adapting End-to-End Speech Recognition for Readable Subtitles
http://arxiv.org/abs/2005.12143
AUTHORS:        Danni Liu ; Jan Niehues ; Gerasimos Spanakis
COMMENTS:       IWSLT 2020
HIGHLIGHT:      The experiments show that with limited data far less than needed for training a model from scratch, we can adapt a Transformer-based ASR model to incorporate both transcription and compression capabilities.

102, TITLE:     AGVNet: Attention Guided Velocity Learning for 3D Human Motion Prediction
http://arxiv.org/abs/2005.12155
AUTHORS:        Xiaoli Liu ; Jianqin Yin ; Jun Liu
HIGHLIGHT:      In this paper, we propose a novel feedforward network, AGVNet (Attention Guided Velocity Learning Network), to predict future poses, which explicitly models the velocities at both Encoder and Decoder.

103, TITLE:     NENET: An Edge Learnable Network for Link Prediction in Scene Text
http://arxiv.org/abs/2005.12147
AUTHORS:        Mayank Kumar Singh ; Sayan Banerjee ; Shubhasis Chaudhuri
COMMENTS:       9 pages
HIGHLIGHT:      This necessitates the need to link adjacent characters, which we propose in this paper using a novel Graph Neural Network (GNN) architecture that allows us to learn both node and edge features as opposed to only the node features under the typical GNN.

104, TITLE:     Formal Methods with a Touch of Magic
http://arxiv.org/abs/2005.12175
AUTHORS:        Parand Alizadeh Alamdari ; Guy Avni ; Thomas A. Henzinger ; Anna Lukina
HIGHLIGHT:      In this work, we address the controller-design problem with a combination of techniques from both fields.

105, TITLE:     Depth-2 QAC circuits cannot simulate quantum parity
http://arxiv.org/abs/2005.12169
AUTHORS:        Daniel Padé ; Stephen Fenner ; Daniel Grier ; Thomas Thierauf
COMMENTS:       21 pages, 2 figures
HIGHLIGHT:      We show that the quantum parity gate on $n > 3$ qubits cannot be cleanly simulated by a quantum circuit with two layers of arbitrary C-SIGN gates of any arity and arbitrary 1-qubit unitary gates, regardless of the number of allowed ancilla qubits.

106, TITLE:     AMR quality rating with a lightweight CNN
http://arxiv.org/abs/2005.12187
AUTHORS:        Juri Opitz
HIGHLIGHT:      In our experiments, we show that the method can rate the quality of AMR graphs more accurately than a strong baseline, with respect to several dimensions of interest.

107, TITLE:     Automating the Surveillance of Mosquito Vectors from Trapped Specimens Using Computer Vision Techniques
http://arxiv.org/abs/2005.12188
AUTHORS:        Mona Minakshi ; Pratool Bharti ; Willie B. McClinton III ; Jamshidbek Mirzakhalov ; Ryan M. Carney ; Sriram Chellappan
HIGHLIGHT:      This paper presents an innovative solution to this problem.

108, TITLE:     Monitoring and Diagnosability of Perception Systems
http://arxiv.org/abs/2005.11816
AUTHORS:        Pasquale Antonante ; David I. Spivak ; Luca Carlone
HIGHLIGHT:      In this work, we propose a mathematical model for runtime monitoring and fault detection of perception systems.

109, TITLE:     Learning visual servo policies via planner cloning
http://arxiv.org/abs/2005.11810
AUTHORS:        Ulrich Viereck ; Kate Saenko ; Robert Platt
HIGHLIGHT:      We propose Penalized Q Cloning (PQC), a new behavior cloning algorithm.

110, TITLE:     Recognizing Families through Images with Pretrained Encoder
http://arxiv.org/abs/2005.11811
AUTHORS:        Tuan-Duy H. Nguyen ; Huu-Nghia H. Nguyen ; Hieu Dao
COMMENTS:       Will appear as part of RFIW2020 in the Proceedings of 2020 International Conference on Automatic Face and Gesture Recognition (IEEE AMFG)
HIGHLIGHT:      We employ 3 methods, FaceNet, Siamese VGG-Face, and a combination of FaceNet and VGG-Face models as feature extractors, to achieve the 9th standing for kinship verification and the 5th standing for kinship retrieval in the Recognizing Family in The Wild 2020 competition.

111, TITLE:     A Proof Assistant Based Formalisation of Core Erlang
http://arxiv.org/abs/2005.11821
AUTHORS:        Péter Bereczky ; Dániel Horpácsi ; Simon Thompson
COMMENTS:       21st International Symposium on Trends in Functional Programming
HIGHLIGHT:      In this paper, we present our proof-assistant-based formalisation of a subset of Erlang, intended to serve as a base for proving refactorings correct.

112, TITLE:     Tezla, an Intermediate Representation for Static Analysis of Michelson Smart Contracts
http://arxiv.org/abs/2005.11839
AUTHORS:        João Santos Reis ; Paul Crocker ; Simão Melo de Sousa
HIGHLIGHT:      This paper introduces Tezla, an intermediate representation of Michelson smart contracts that eases the design of static smart contract analysers.

113, TITLE:     How Does That Sound? Multi-Language SpokenName2Vec Algorithm Using Speech Generation and Deep Learning
http://arxiv.org/abs/2005.11838
AUTHORS:        Aviad Elyashar ; Rami Puzis ; Michael Fire
HIGHLIGHT:      In this paper, we propose SpokenName2Vec, a novel and generic approach which addresses the similar name suggestion problem by utilizing automated speech generation, and deep learning to produce spoken name embeddings.

114, TITLE:     Stronger Baselines for Grammatical Error Correction Using Pretrained Encoder-Decoder Model
http://arxiv.org/abs/2005.11849
AUTHORS:        Satoru Katsumata ; Mamoru Komachi
HIGHLIGHT:      In this study, we explored two generic pretrained encoder-decoder (Enc-Dec) models, including BART, which reported the state-of-the-art (SOTA) results for several Seq2Seq tasks other than GEC.

115, TITLE:     An efficient iterative method for reconstructing surface from point clouds
http://arxiv.org/abs/2005.11864
AUTHORS:        Dong Wang
COMMENTS:       23 pages, 15 figures
HIGHLIGHT:      In this paper, we develop an efficient iterative method on a variational model for the surface reconstruction from point clouds.

116, TITLE:     ON-TRAC Consortium for End-to-End and Simultaneous Speech Translation Challenge Tasks at IWSLT 2020
http://arxiv.org/abs/2005.11861
AUTHORS:        Maha Elbayad ; Ha Nguyen ; Fethi Bougares ; Natalia Tomashenko ; Antoine Caubrière ; Benjamin Lecouteux ; Yannick Estève ; Laurent Besacier
HIGHLIGHT:      We propose an algorithm to control the latency of the ASR+MT cascade and achieve a good latency-quality trade-off on both subtasks.

117, TITLE:     Bayesian Conditional GAN for MRI Brain Image Synthesis
http://arxiv.org/abs/2005.11875
AUTHORS:        Gengyan Zhaoa ; Mary E. Meyerand ; Rasmus M. Birn
COMMENTS:       26 pages, 7 figures
HIGHLIGHT:      In this work, we propose to use Bayesian conditional generative adversarial network (GAN) with concrete dropout to improve image synthesis accuracy.

118, TITLE:     Novel Human-Object Interaction Detection via Adversarial Domain Generalization
http://arxiv.org/abs/2005.11406
AUTHORS:        Yuhang Song ; Wenbo Li ; Lei Zhang ; Jianwei Yang ; Emre Kiciman ; Hamid Palangi ; Jianfeng Gao ; C. -C. Jay Kuo ; Pengchuan Zhang
HIGHLIGHT:      We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios.

119, TITLE:     AutoSUM: Automating Feature Extraction and Multi-user Preference Simulation for Entity Summarization
http://arxiv.org/abs/2005.11888
AUTHORS:        Dongjun Wei ; Yaxin Liu ; Fuqing Zhu ; Liangjun Zang ; Wei Zhou ; Yijun Lu ; Songlin Hu
COMMENTS:       11 pages, accepted in PAKDD'2020
HIGHLIGHT:      In this paper, a novel integration method called AutoSUM is proposed for automatic feature extraction and multi-user preference simulation to overcome the drawbacks of previous methods.

120, TITLE:     One of these (Few) Things is Not Like the Others
http://arxiv.org/abs/2005.11405
AUTHORS:        Nat Roth ; Justin Wagle
HIGHLIGHT:      We propose a model which can both classify new images based on a small number of examples and recognize images which do not belong to any previously seen group.

121, TITLE:     Sentiment Analysis: Detecting Valence, Emotions, and Other Affectual States from Text
http://arxiv.org/abs/2005.11882
AUTHORS:        Saif M. Mohammad
COMMENTS:       This is the author's manuscript of what is slated to appear in the Second Edition of Emotion Measurement, 2020
HIGHLIGHT:      This article presents a sweeping overview of sentiment analysis research that includes: the origins of the field, the rich landscape of tasks, challenges, a survey of the methods and resources used, and applications.

122, TITLE:     Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
http://arxiv.org/abs/2005.11401
AUTHORS:        Patrick Lewis ; Ethan Perez ; Aleksandara Piktus ; Fabio Petroni ; Vladimir Karpukhin ; Naman Goyal ; Heinrich Küttler ; Mike Lewis ; Wen-tau Yih ; Tim Rocktäschel ; Sebastian Riedel ; Douwe Kiela
HIGHLIGHT:      We introduce RAG models where the parametric memory is a pre-trained seq2seq model and the non-parametric memory is a dense vector index of Wikipedia, accessed with a pre-trained neural retriever.

123, TITLE:     Approaching Bio Cellular Classification for Malaria Infected Cells Using Machine Learning and then Deep Learning to compare & analyze K-Nearest Neighbours and Deep CNNs
http://arxiv.org/abs/2005.11417
AUTHORS:        Rishabh Malhotra ; Dhron Joshi ; Ku Young Shin
COMMENTS:       7 Pages
HIGHLIGHT:      Here, we implement two models of classification; a convolutional neural network, and the k nearest neighbours algorithm.

124, TITLE:     Reinforcement Learning with Iterative Reasoning for Merging in Dense Traffic
http://arxiv.org/abs/2005.11895
AUTHORS:        Maxime Bouton ; Alireza Nakhaei ; David Isele ; Kikuo Fujimura ; Mykel J. Kochenderfer
COMMENTS:       6pages, 5 figures
HIGHLIGHT:      In this work, we propose a combination of reinforcement learning and game theory to learn merging behaviors.

125, TITLE:     Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers
http://arxiv.org/abs/2005.11787
AUTHORS:        Anne Lauscher ; Olga Majewska ; Leonardo F. R. Ribeiro ; Iryna Gurevych ; Nikolai Rozanov ; Goran Glavaš
HIGHLIGHT:      In this work, we investigate models for complementing the distributional knowledge of BERT with conceptual knowledge from ConceptNet and its corresponding Open Mind Common Sense (OMCS) corpus, respectively, using adapter training.

126, TITLE:     Vision-based control of a knuckle boom crane with online cable length estimation
http://arxiv.org/abs/2005.11794
AUTHORS:        Geir Ole Tysse ; Andrej Cibicik ; Olav Egeland
COMMENTS:       This paper was first submitted to IEEE/ASME Transactions on Mechatronics
HIGHLIGHT:      The length of the payload cable is also estimated using a least squares technique with projection.

127, TITLE:     Walrasian Equilibria in Markets with Small Demands
http://arxiv.org/abs/2005.11796
AUTHORS:        Argyrios Deligkas ; Themistoklis Melissourgos ; Paul G. Spirakis
HIGHLIGHT:      Put differently, we give the first parallel algorithm that finds a Walrasian equilibrium in polylogarithmic time.

128, TITLE:     Functional Space Variational Inference for Uncertainty Estimation in Computer Aided Diagnosis
http://arxiv.org/abs/2005.11797
AUTHORS:        Pranav Poduval ; Hrushikesh Loya ; Amit Sethi
COMMENTS:       Meaningful priors on the functional space rather than the weight space, result in well calibrated uncertainty estimates
HIGHLIGHT:      In this work, by taking skin lesion classification as an example task, we show that by shifting Bayesian inference to the functional space we can craft meaningful priors that give better calibrated uncertainty estimates at a much lower computational cost.

129, TITLE:     Pulmonary Nodule Malignancy Classification Using its Temporal Evolution with Two-Stream 3D Convolutional Neural Networks
http://arxiv.org/abs/2005.11341
AUTHORS:        Xavier Rafael-Palou ; Anton Aubanell ; Ilaria Bonavita ; Mario Ceresa ; Gemma Piella ; Vicent Ribas ; Miguel A. González Ballester
HIGHLIGHT:      In this work, we propose a two-stream 3D convolutional neural network that predicts malignancy by jointly analyzing two pulmonary nodule volumes from the same patient taken at different time-points.

130, TITLE:     Single-Agent Optimization Through Policy Iteration Using Monte-Carlo Tree Search
http://arxiv.org/abs/2005.11335
AUTHORS:        Arta Seify ; Michael Buro
COMMENTS:       Poster presentation at RL in Games Workshop, AAAI 2020
HIGHLIGHT:      In this paper, we describe a search algorithm that uses a variant of MCTS which we enhanced by 1) a novel action value normalization mechanism for games with potentially unbounded rewards (which is the case in many optimization problems), 2) defining a virtual loss function that enables effective search parallelization, and 3) a policy network, trained by generations of self-play, to guide the search.

131, TITLE:     SentPWNet: A Unified Sentence Pair Weighting Network for Task-specific Sentence Embedding
http://arxiv.org/abs/2005.11347
AUTHORS:        Li Zhang ; Han Wang ; Lingxiao Li
HIGHLIGHT:      In this paper, our theoretical analysis shows that existing works severely suffered from a good pair sampling and instance weighting strategy.

132, TITLE:     The Discussion Tracker Corpus of Collaborative Argumentation
http://arxiv.org/abs/2005.11344
AUTHORS:        Christopher Olshefski ; Luca Lugini ; Ravneet Singh ; Diane Litman ; Amanda Godley
COMMENTS:       In Proceedings of The 12th Language Resources and Evaluation Conference (LREC), Marseille, France, May 2020
HIGHLIGHT:      The transcripts were annotated for three dimensions of collaborative argumentation: argument moves (claims, evidence, and explanations), specificity (low, medium, high) and collaboration (e.g., extensions of and disagreements about others' ideas). In addition to providing descriptive statistics on the corpus, we provide performance benchmarks and associated code for predicting each dimension separately, illustrate the use of the multiple annotations in the corpus to improve performance via multi-task learning, and finally discuss other ways the corpus might be used to further NLP research.

133, TITLE:     Stable and expressive recurrent vision models
http://arxiv.org/abs/2005.11362
AUTHORS:        Drew Linsley ; Alekh Karkada Ashok ; Lakshmi Narasimhan Govindarajan ; Rex Liu ; Thomas Serre
HIGHLIGHT:      Here, we develop a new learning algorithm, "contractor recurrent back-propagation" (C-RBP), which alleviates these issues by achieving constant O(1) memory-complexity with steps of recurrent processing.

134, TITLE:     Open-Retrieval Conversational Question Answering
http://arxiv.org/abs/2005.11364
AUTHORS:        Chen Qu ; Liu Yang ; Cen Chen ; Minghui Qiu ; W. Bruce Croft ; Mohit Iyyer
COMMENTS:       Accepted to SIGIR'20
HIGHLIGHT:      To address this limitation, we introduce an open-retrieval conversational question answering (ORConvQA) setting, where we learn to retrieve evidence from a large collection before extracting answers, as a further step towards building functional conversational search systems.

135, TITLE:     JSSR: A Joint Synthesis, Segmentation, and Registration System for 3D Multi-Modal Image Alignment of Large-scale Pathological CT Scans
http://arxiv.org/abs/2005.12209
AUTHORS:        Fengze Liu ; Jingzheng Cai ; Yuankai Huo ; Le Lu ; Adam P Harrison
HIGHLIGHT:      In this work, we propose a novel multi-task learning system, JSSR, based on an end-to-end 3D convolutional neural network that is composed of a generator, a register and a segmentor, for the tasks of synthesis, registration and segmentation, respectively.

136, TITLE:     Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation
http://arxiv.org/abs/2005.11355
AUTHORS:        Aakanksha Naik ; Carolyn Rosé
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      Our work leverages the adversarial domain adaptation (ADA) framework to introduce domain-invariance.

137, TITLE:     Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net
http://arxiv.org/abs/2005.11368
AUTHORS:        Amartya Kalapahar ; Julio Silva-Rodríguez ; Adrián Colomer ; Fernando López-Mir ; Valery Naranjo
HIGHLIGHT:      Gleason Grading of Histology Prostate Images through Semantic Segmentation via Residual U-Net

138, TITLE:     Image Translation by Latent Union of Subspaces for Cross-Domain Plaque Detection
http://arxiv.org/abs/2005.11384
AUTHORS:        Yingying Zhu ; Daniel C. Elton ; Sungwon Lee ; Perry J. Pickhardt ; Ronald M. Summers
COMMENTS:       accepted as a short paper in the 2020 Medical Imaging with Deep Learning (MIDL) conference
HIGHLIGHT:      Motivated by this, we propose an image translation network using a shared union of subspaces constraint and show our approach preserves subtle structures (plaques) better than the conventional method.

139, TITLE:     A review of sentiment analysis research in Arabic language
http://arxiv.org/abs/2005.12240
AUTHORS:        Oumaima Oueslati ; Erik Cambria ; Moez Ben HajHmida ; Habib Ounelli
HIGHLIGHT:      In this paper, we carry out an in-depth qualitative study of the most important research works in this context by presenting limits and strengths of existing approaches.

140, TITLE:     Dynamic Value Estimation for Single-Task Multi-Scene Reinforcement Learning
http://arxiv.org/abs/2005.12254
AUTHORS:        Jaskirat Singh ; Liang Zheng
HIGHLIGHT:      To this end, we propose a dynamic value estimation (DVE) technique for these multiple-MDP environments, motivated by the clustering effect observed in the value function distribution across different scenes.

141, TITLE:     Attention-based Neural Bag-of-Features Learning for Sequence Data
http://arxiv.org/abs/2005.12250
AUTHORS:        Dat Thanh Tran ; Nikolaos Passalis ; Anastasios Tefas ; Moncef Gabbouj ; Alexandros Iosifidis
HIGHLIGHT:      In this paper, we propose 2D-Attention (2DA), a generic attention formulation for sequence data, which acts as a complementary computation block that can detect and focus on relevant sources of information for the given learning objective.

142, TITLE:     Demoting Racial Bias in Hate Speech Detection
http://arxiv.org/abs/2005.12246
AUTHORS:        Mengzhou Xia ; Anjalie Field ; Yulia Tsvetkov
COMMENTS:       Accepted at SocialNLP Workshop @ACL 2020
HIGHLIGHT:      In this paper, we use adversarial training to mitigate this bias, introducing a hate speech classifier that learns to detect toxic sentences while demoting confounds corresponding to AAE texts.

143, TITLE:     Neural Topological SLAM for Visual Navigation
http://arxiv.org/abs/2005.12256
AUTHORS:        Devendra Singh Chaplot ; Ruslan Salakhutdinov ; Abhinav Gupta ; Saurabh Gupta
COMMENTS:       Published in CVPR 2020. See the project webpage at https://devendrachaplot.github.io/projects/Neural-Topological-SLAM
HIGHLIGHT:      We describe supervised learning-based algorithms that can build, maintain and use such representations under noisy actuation.



==========Updates to Previous Papers==========
1, TITLE:       RelDenClu:A Relative Density based Biclustering Method for identifying non-linear feature relations with an Application to identify factors effecting spread of COVID-19
http://arxiv.org/abs/1811.04661
AUTHORS:        Namita Jain ; Susmita Ghosh ; C. A. Murthy
HIGHLIGHT:      The proposed method, RelDenClu uses the local variations in marginal and joint densities for each pair of features to find the subset of observations, which forms the bases of the relation between them.

2, TITLE:       A case study of Consistent Vehicle Routing Problem with Time Windows
http://arxiv.org/abs/1912.05929
AUTHORS:        Hernán Lespay ; Karol Suchan
HIGHLIGHT:      We develop a heuristic solution method for the Consistent Vehicle Routing Problem with Time Windows (ConVRPTW), motivated by a real-world application at a distribution center of a food company.

3, TITLE:       A type of generalization error induced by initialization in deep neural networks
http://arxiv.org/abs/1905.07777
AUTHORS:        Yaoyu Zhang ; Zhi-Qin John Xu ; Tao Luo ; Zheng Ma
COMMENTS:       Accepted by MSML
HIGHLIGHT:      In this work, by exploiting the linearity of DNN training dynamics in the NTK regime \citep{jacot2018neural,lee2019wide}, we provide an explicit and quantitative answer to this problem.

4, TITLE:       Learning Spatial-Spectral Prior for Super-Resolution of Hyperspectral Imagery
http://arxiv.org/abs/2005.08752
AUTHORS:        Junjun Jiang ; He Sun ; Xianming Liu ; Jiayi Ma
COMMENTS:       Accepted for publication at IEEE Transactions on Computational Imaging
HIGHLIGHT:      In this paper, we make a step forward by investigating how to adapt state-of-the-art residual learning based single gray/RGB image super-resolution approaches for computationally efficient single hyperspectral image super-resolution, referred as SSPSR.

5, TITLE:       Cell Segmentation and Tracking using CNN-Based Distance Predictions and a Graph-Based Matching Strategy
http://arxiv.org/abs/2004.01486
AUTHORS:        Tim Scherr ; Katharina Löffler ; Moritz Böhland ; Ralf Mikut
COMMENTS:       16 pages, 14 figures, methods of the team KIT-Sch-GE for the IEEE ISBI 2020 Cell Tracking Challenge
HIGHLIGHT:      In this paper, we present a method for the segmentation of touching cells in microscopy images.

6, TITLE:       NPLDA: A Deep Neural PLDA Model for Speaker Verification
http://arxiv.org/abs/2002.03562
AUTHORS:        Shreyas Ramoji ; Prashant Krishnan ; Sriram Ganapathy
COMMENTS:       Published in Odyssey 2020, the Speaker and Language Recognition Workshop (VOiCES Special Session). Link to GitHub Implementation: https://github.com/iiscleap/NeuralPlda. arXiv admin note: substantial text overlap with arXiv:2001.07034
HIGHLIGHT:      In this work, we propose a neural network approach for backend modeling in speaker recognition.

7, TITLE:       On the Idiosyncrasies of the Mandarin Chinese Classifier System
http://arxiv.org/abs/1902.10193
AUTHORS:        Shijia Liu ; Hongyuan Mei ; Adina Williams ; Ryan Cotterell
HIGHLIGHT:      In this paper, we introduce an information-theoretic approach to measuring idiosyncrasy; we examine how much the uncertainty in Mandarin Chinese classifiers can be reduced by knowing semantic information about the nouns that the classifiers modify.

8, TITLE:       SEAN: Image Synthesis with Semantic Region-Adaptive Normalization
http://arxiv.org/abs/1911.12861
AUTHORS:        Peihao Zhu ; Rameen Abdal ; Yipeng Qin ; Peter Wonka
COMMENTS:       Accepted as a CVPR 2020 oral paper. The interactive demo is available at https://youtu.be/0Vbj9xFgoUw
HIGHLIGHT:      We propose semantic region-adaptive normalization (SEAN), a simple but effective building block for Generative Adversarial Networks conditioned on segmentation masks that describe the semantic regions in the desired output image.

9, TITLE:       Using Computer Vision to enhance Safety of Workforce in Manufacturing in a Post COVID World
http://arxiv.org/abs/2005.05287
AUTHORS:        Prateek Khandelwal ; Anuj Khandelwal ; Snigdha Agarwal ; Deep Thomas ; Naveen Xavier ; Arun Raghuraman
COMMENTS:       6 pages, 7 figure, 1 table
HIGHLIGHT:      This paper describes an efficient and economic approach of using AI to create a safe environment in a manufacturing setup.

10, TITLE:      Network Representation Learning for Link Prediction: Are we improving upon simple heuristics?
http://arxiv.org/abs/2002.11522
AUTHORS:        Alexandru Mara ; Jefrey Lijffijt ; Tijl De Bie
HIGHLIGHT:      In this work, we analyse 17 network embedding methods on 7 real-world datasets and find, using a consistent evaluation pipeline, only thin progress over the recent years.

11, TITLE:      Wake Word Detection with Alignment-Free Lattice-Free MMI
http://arxiv.org/abs/2005.08347
AUTHORS:        Yiming Wang ; Hang Lv ; Daniel Povey ; Lei Xie ; Sanjeev Khudanpur
COMMENTS:       Submitted to Interspeech 2020. 5 pages, 3 figures
HIGHLIGHT:      We present novel methods to train a hybrid DNN/HMM wake word detection system from partially labeled training data, and to use it in on-line applications: (i) we remove the prerequisite of frame-level alignments in the LF-MMI training algorithm, permitting the use of un-transcribed training examples that are annotated only for the presence/absence of the wake word; (ii) we show that the classical keyword/filler model must be supplemented with an explicit non-speech (silence) model for good performance; (iii) we present an FST-based decoder to perform online detection.

12, TITLE:      Learning to Ask Medical Questions using Reinforcement Learning
http://arxiv.org/abs/2004.00994
AUTHORS:        Uri Shaham ; Tom Zahavy ; Cesar Caraballo ; Shiwani Mahajan ; Daisy Massey ; Harlan Krumholz
HIGHLIGHT:      We propose a novel reinforcement learning-based approach for adaptive and iterative feature selection.

13, TITLE:      Long-Term Progress and Behavior Complexification in Competitive Co-Evolution
http://arxiv.org/abs/1909.08303
AUTHORS:        Luca Simione ; Stefano Nolfi
HIGHLIGHT:      We propose a new competitive algorithm that produces long-term global progress by identifying and by filtering out opportunistic variations, i.e. variations leading to progress against current competitors and retrogression against other competitors.

14, TITLE:      MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
http://arxiv.org/abs/2005.10582
AUTHORS:        Yiyang Shen ; Yidan Feng ; Sen Deng ; Dong Liang ; Jing Qin ; Haoran Xie ; Mingqiang Wei
HIGHLIGHT:      We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.

15, TITLE:      Should Answer Immediately or Wait for Further Information? A Novel Wait-or-Answer Task and Its Predictive Approach
http://arxiv.org/abs/2002.09616
AUTHORS:        Zehao Lin ; Shaobo Cui ; Xiaoming Kang ; Guodun Li ; Feng Ji ; Haiqing Chen ; Yin Zhang
HIGHLIGHT:      Motivated by such interesting quandary, we define a novel task: Wait-or-Answer to better tackle this dilemma faced by dialogue systems.

16, TITLE:      A Simple Language Model for Task-Oriented Dialogue
http://arxiv.org/abs/2005.00796
AUTHORS:        Ehsan Hosseini-Asl ; Bryan McCann ; Chien-Sheng Wu ; Semih Yavuz ; Richard Socher
COMMENTS:       Version 2: Adding error analysis; 20 Pages, 1 figure, 18 tables
HIGHLIGHT:      SimpleTOD is a simple approach to task-oriented dialogue that uses a single causal language model trained on all sub-tasks recast as a single sequence prediction problem.

17, TITLE:      A novel centroid update approach for clustering-based superpixel methods and superpixel-based edge detection
http://arxiv.org/abs/1910.08439
AUTHORS:        Houwang Zhang ; Chong Wu ; Le Zhang ; Hanying Zheng
COMMENTS:       This paper has been accepted by ICIP2020
HIGHLIGHT:      Besides, we propose a novel superpixel-based edge detection method.

18, TITLE:      A Comparison of Label-Synchronous and Frame-Synchronous End-to-End Models for Speech Recognition
http://arxiv.org/abs/2005.10113
AUTHORS:        Linhao Dong ; Cheng Yi ; Jianzong Wang ; Shiyu Zhou ; Shuang Xu ; Xueli Jia ; Bo Xu
COMMENTS:       4 pages, 2 figures
HIGHLIGHT:      In this work, we make a detailed comparison on a representative label-synchronous model (transformer) and a soft frame-synchronous model (continuous integrate-and-fire (CIF) based model).

19, TITLE:      Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images
http://arxiv.org/abs/2005.03832
AUTHORS:        Kelei He ; Wei Zhao ; Xingzhi Xie ; Wen Ji ; Mingxia Liu ; Zhenyu Tang ; Feng Shi ; Yang Gao ; Jun Liu ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification.

20, TITLE:      Predicting Online Item-choice Behavior: A Shape-restricted Regression Perspective
http://arxiv.org/abs/2004.08519
AUTHORS:        Naoki Nishimura ; Noriyoshi Sukegawa ; Yuichi Takano ; Jiro Iwanaga
HIGHLIGHT:      We propose a shape-restricted optimization model that accurately estimates item-choice probabilities for all possible PV sequences.

21, TITLE:      Establishing Strong Baselines for the New Decade: Sequence Tagging, Syntactic and Semantic Parsing with BERT
http://arxiv.org/abs/1908.04943
AUTHORS:        Han He ; Jinho D. Choi
COMMENTS:       Accepted to the International Florida Artificial Intelligence Research Society Conference, FLAIRS 2020
HIGHLIGHT:      This paper presents new state-of-the-art models for three tasks, part-of-speech tagging, syntactic parsing, and semantic parsing, using the cutting-edge contextualized embedding framework known as BERT.

22, TITLE:      Approximation Algorithms for Multi-Robot Patrol-Scheduling with Min-Max Latency
http://arxiv.org/abs/2005.02530
AUTHORS:        Peyman Afshani ; Mark De Berg ; Kevin Buchin ; Jie Gao ; Maarten Loffler ; Amir Nayyeri ; Benjamin Raichel ; Rik Sarkar ; Haotian Wang ; Hao-Tsung Yang
COMMENTS:       Proceedings of the 14th International Workshop on the Algorithmic Foundations of Robotics (WAFR 20)
HIGHLIGHT:      We present a polynomial-time algorithm with an approximation factor of $O(k^2 \log \frac{w_{\max}}{w_{\min}})$ to the optimal solution, where $w_{\max}$ and $w_{\min}$ are the maximum and minimum weight of the sites respectively.

23, TITLE:      Understanding Graph Isomorphism Network for rs-fMRI Functional Connectivity Analysis
http://arxiv.org/abs/2001.03690
AUTHORS:        Byung-Hoon Kim ; Jong Chul Ye
COMMENTS:       This paper is accepted for Frontiers in Neuroscience
HIGHLIGHT:      One of the important contributions of this paper is the observation that the GIN is a dual representation of convolutional neural network (CNN) in the graph space where the shift operation is defined using the adjacency matrix.

24, TITLE:      Classification of Arrhythmia by Using Deep Learning with 2-D ECG Spectral Image Representation
http://arxiv.org/abs/2005.06902
AUTHORS:        Amin Ullah ; Syed M. Anwar ; Muhammad Bilal ; Raja M Mehmood
COMMENTS:       14 pages, 5 figures, accepted for future publication in Remote Sensing MDPI Journal
HIGHLIGHT:      In this study, we propose a two-dimensional (2-D) convolutional neural network (CNN) model for the classification of ECG signals into eight classes; namely, normal beat, premature ventricular contraction beat, paced beat, right bundle branch block beat, left bundle branch block beat, atrial premature contraction beat, ventricular flutter wave beat, and ventricular escape beat.

25, TITLE:      Noise Pollution in Hospital Readmission Prediction: Long Document Classification with Reinforcement Learning
http://arxiv.org/abs/2005.01259
AUTHORS:        Liyan Xu ; Julien Hogan ; Rachel E. Patzer ; Jinho D. Choi
COMMENTS:       Accepted to the ACL Workshop on Biomedical Natural Language Processing, BioNLP 2020
HIGHLIGHT:      This paper presents a reinforcement learning approach to extract noise in long clinical documents for the task of readmission prediction after kidney transplant.

26, TITLE:      DJEnsemble: On the Selection of a Disjoint Ensemble of Deep Learning Black-Box Spatio-temporal Models
http://arxiv.org/abs/2005.11093
AUTHORS:        Yania Molina Souto ; Rafael Pereira ; Rocío Zorrilla ; Anderson Chaves ; Brian Tsan ; Florin Rusu ; Eduardo Ogasawara ; Artur Ziviani ; Fabio Porto
HIGHLIGHT:      In this paper, we present a cost-based approach for the automatic selection and allocation of a disjoint ensemble of black-box predictors to answer predictive spatio-temporal queries.

27, TITLE:      A Closer Look at Deep Policy Gradients
http://arxiv.org/abs/1811.02553
AUTHORS:        Andrew Ilyas ; Logan Engstrom ; Shibani Santurkar ; Dimitris Tsipras ; Firdaus Janoos ; Larry Rudolph ; Aleksander Madry
COMMENTS:       ICLR 2020 version
HIGHLIGHT:      To this end, we propose a fine-grained analysis of state-of-the-art methods based on key elements of this framework: gradient estimation, value prediction, and optimization landscapes.

28, TITLE:      CRVOS: Clue Refining Network for Video Object Segmentation
http://arxiv.org/abs/2002.03651
AUTHORS:        Suhwan Cho ; MyeongAh Cho ; Tae-young Chung ; Heansung Lee ; Sangyoun Lee
COMMENTS:       ICIP 2020 camera-ready version
HIGHLIGHT:      In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information.

29, TITLE:      Algorithm for Finding the Maximum Clique Based on Continuous Time Quantum Walk
http://arxiv.org/abs/1912.02728
AUTHORS:        Xi Li ; Mingyou Wu ; Hanwu Chen
HIGHLIGHT:      In this work, we consider the application of continuous time quantum walking(CTQW) to the Maximum Clique(MC) Problem.

30, TITLE:      DRLViz: Understanding Decisions and Memory in Deep Reinforcement Learning
http://arxiv.org/abs/1909.02982
AUTHORS:        Theo Jaunet ; Romain Vuillemot ; Christian Wolf
HIGHLIGHT:      We present DRLViz, a visual analytics interface to interpret the internal memory of an agent (e.g. a robot) trained using deep reinforcement learning.

31, TITLE:      CompLex: A New Corpus for Lexical Complexity Predicition from Likert Scale Data
http://arxiv.org/abs/2003.07008
AUTHORS:        Matthew Shardlow ; Michael Cooper ; Marcos Zampieri
COMMENTS:       Proceedings of the 1st Workshop on Tools and Resources to Empower People with REAding DIfficulties (READI). pp. 57-62
HIGHLIGHT:      With a few exceptions, previous studies have approached the task as a binary classification task in which systems predict a complexity value (complex vs. non-complex) for a set of target words in a text.

32, TITLE:      Principal Neighbourhood Aggregation for Graph Nets
http://arxiv.org/abs/2004.05718
AUTHORS:        Gabriele Corso ; Luca Cavalleri ; Dominique Beaini ; Pietro Liò ; Petar Veličković
HIGHLIGHT:      Accordingly, we propose Principal Neighbourhood Aggregation (PNA), a novel architecture combining multiple aggregators with degree-scalers (which generalize the sum aggregator).

33, TITLE:      Efficient Deep Reinforcement Learning via Adaptive Policy Transfer
http://arxiv.org/abs/2002.08037
AUTHORS:        Tianpei Yang ; Jianye Hao ; Zhaopeng Meng ; Zongzhang Zhang ; Yujing Hu ; Yingfeng Cheng ; Changjie Fan ; Weixun Wang ; Wulong Liu ; Zhaodong Wang ; Jiajie Peng
COMMENTS:       Accepted by IJCAI'2020
HIGHLIGHT:      In this paper, we propose a novel Policy Transfer Framework (PTF) to accelerate RL by taking advantage of this idea.

34, TITLE:      Learning Goal-Oriented Visual Dialog via Tempered Policy Gradient
http://arxiv.org/abs/1807.00737
AUTHORS:        Rui Zhao ; Volker Tresp
COMMENTS:       Published in IEEE Spoken Language Technology (SLT 2018), Athens, Greece
HIGHLIGHT:      To mitigate this problem, we propose a class of novel temperature-based extensions for policy gradient methods, which are referred to as Tempered Policy Gradients (TPGs).

35, TITLE:      Simple Dataset for Proof Method Recommendation in Isabelle/HOL (Dataset Description)
http://arxiv.org/abs/2004.10667
AUTHORS:        Yutaka Nagashima
COMMENTS:       This is the preprint of our short paper accepted at the 13th Conference on Intelligent Computer Mathematics (CICM 2020)
HIGHLIGHT:      In this data description, we present a simple dataset that contains data on over 400k proof method applications along with over 100 extracted features for each in a format that can be processed easily without any knowledge about formal logic.

36, TITLE:      LEAP System for SRE19 CTS Challenge -- Improvements and Error Analysis
http://arxiv.org/abs/2002.02735
AUTHORS:        Shreyas Ramoji ; Prashant Krishnan ; Bhargavram Mysore ; Prachi Singh ; Sriram Ganapathy
COMMENTS:       Published In Proc. Odyssey 2020, the Speaker and Language Recognition Workshop. Link to GitHub Implementation: https://github.com/iiscleap/NeuralPlda
HIGHLIGHT:      In this paper, we provide a detailed account of the LEAP SRE system submitted to the CTS challenge focusing on the novel components in the back-end system modeling.

37, TITLE:      Improved BiGAN training with marginal likelihood equalization
http://arxiv.org/abs/1911.01425
AUTHORS:        Pablo Sánchez-Martín ; Pablo M. Olmos ; Fernando Perez-Cruz
HIGHLIGHT:      We propose a novel training procedure for improving the performance of generative adversarial networks (GANs), especially to bidirectional GANs.

38, TITLE:      Understanding Contrastive Representation Learning through Alignment and Uniformity on the Hypersphere
http://arxiv.org/abs/2005.10242
AUTHORS:        Tongzhou Wang ; Phillip Isola
HIGHLIGHT:      In this work, we identify two key properties related to the contrastive loss: (1) alignment (closeness) of features from positive pairs, and (2) uniformity of the induced distribution of the (normalized) features on the hypersphere.

39, TITLE:      HUMBI: A Large Multiview Dataset of Human Body Expressions
http://arxiv.org/abs/1812.00281
AUTHORS:        Zhixuan Yu ; Jae Shin Yoon ; In Kyu Lee ; Prashanth Venkatesh ; Jaesik Park ; Jihun Yu ; Hyun Soo Park
HIGHLIGHT:      This paper presents a new large multiview dataset called HUMBI for human body expressions with natural clothing.

40, TITLE:      Improving the Explainability of Neural Sentiment Classifiers via Data Augmentation
http://arxiv.org/abs/1909.04225
AUTHORS:        Hanjie Chen ; Yangfeng Ji
COMMENTS:       11 pages, NeurIPS 2019 Workshop on Robust AI in Financial Services
HIGHLIGHT:      In this work, we study the problem of improving the explainability of existing sentiment classifiers.

41, TITLE:      GPO: Global Plane Optimization for Fast and Accurate Monocular SLAM Initialization
http://arxiv.org/abs/2004.12051
AUTHORS:        Sicong Du ; Hengkai Guo ; Yao Chen ; Yilun Lin ; Xiangbing Meng ; Linfu Wen ; Fei-Yue Wang
COMMENTS:       Revised some minor errors. Accepted by ICRA 2020
HIGHLIGHT:      This paper focuses on a novel initialization method for monocular SLAM based on planar features.

42, TITLE:      Time Reversal as Self-Supervision
http://arxiv.org/abs/1810.01128
AUTHORS:        Suraj Nair ; Mohammad Babaeizadeh ; Chelsea Finn ; Sergey Levine ; Vikash Kumar
COMMENTS:       7 pages, 10 figures
HIGHLIGHT:      We propose a novel self-supervision technique that uses time-reversal to learn goals and provide a high level plan to reach them.

43, TITLE:      All Word Embeddings from One Embedding
http://arxiv.org/abs/2004.12073
AUTHORS:        Sho Takase ; Sosuke Kobayashi
HIGHLIGHT:      In this study, to reduce the total number of parameters, the embeddings for all words are represented by transforming a shared embedding.

44, TITLE:      Detecting Local Insights from Global Labels: Supervised & Zero-Shot Sequence Labeling via a Convolutional Decomposition
http://arxiv.org/abs/1906.01154
AUTHORS:        Allen Schmaltz
COMMENTS:       72 pages, 40 tables, 6 figures (main: 50 pages, 23 tables, 6 figures; appendix: 22 pages, 17 tables). minor update (no methodological changes): journal format; additional datasets (sentiment) and experiments (out-of-domain); previously reported results (as with earlier version iterations) are unchanged
HIGHLIGHT:      Specifically, we present and analyze binary labeling via a convolutional decomposition (BLADE), a sequence labeling approach based on a decomposition of the filter-ngram interactions of a convolutional neural network and a linear layer.

45, TITLE:      Dynamic Convolutions: Exploiting Spatial Sparsity for Faster Inference
http://arxiv.org/abs/1912.03203
AUTHORS:        Thomas Verelst ; Tinne Tuytelaars
COMMENTS:       CVPR 2020 (poster) https://github.com/thomasverelst/dynconv
HIGHLIGHT:      To address this inefficiency, we propose a method to dynamically apply convolutions conditioned on the input image.

46, TITLE:      Saving the Sonorine: Photovisual Audio Recovery Using Image Processing and Computer Vision Techniques
http://arxiv.org/abs/2005.08944
AUTHORS:        Kevin Feng
COMMENTS:       29 pages (including cover), 23 figures
HIGHLIGHT:      This paper presents a novel technique to recover audio from sonorines, an early 20th century form of analogue sound storage.

47, TITLE:      MATINF: A Jointly Labeled Large-Scale Dataset for Classification, Question Answering and Summarization
http://arxiv.org/abs/2004.12302
AUTHORS:        Canwen Xu ; Jiaxin Pei ; Hongtao Wu ; Yiyu Liu ; Chenliang Li
COMMENTS:       Accepted as a long paper at ACL 2020
HIGHLIGHT:      We propose MATINF, the first jointly labeled large-scale dataset for classification, question answering and summarization.

48, TITLE:      Insignificant Choice Polynomial Time
http://arxiv.org/abs/2005.04598
AUTHORS:        Klaus-Dieter Schewe
COMMENTS:       46 pages
HIGHLIGHT:      We use this result for our second contribution showing that PTIME differs from NP.

49, TITLE:      Joint Liver Lesion Segmentation and Classification via Transfer Learning
http://arxiv.org/abs/2004.12352
AUTHORS:        Michal Heker ; Hayit Greenspan
COMMENTS:       Accepted to MIDL 2020
HIGHLIGHT:      In this work, we study the combination of these two approaches for the problem of liver lesion segmentation and classification.

50, TITLE:      Computationally efficient sparse clustering
http://arxiv.org/abs/2005.10817
AUTHORS:        Matthias Löffler ; Alexander S. Wein ; Afonso S. Bandeira
COMMENTS:       26 pages
HIGHLIGHT:      Compared to these existing results, we cover a wider set of parameter regimes and give a more precise understanding of the runtime required and the misclustering error achievable.

51, TITLE:      Multiparty Session Programming with Global Protocol Combinators
http://arxiv.org/abs/2005.06333
AUTHORS:        Keigo Imai ; Rumyana Neykova ; Nobuko Yoshida ; Shoji Yuen
COMMENTS:       ECOOP 2020
HIGHLIGHT:      To overcome these limitations, we propose a library for programming with global combinators -- a set of functions for writing and verifying multiparty protocols in OCaml.

52, TITLE:      Logic-Guided Data Augmentation and Regularization for Consistent Question Answering
http://arxiv.org/abs/2004.10157
AUTHORS:        Akari Asai ; Hannaneh Hajishirzi
COMMENTS:       Published as a conference paper at ACL 2020
HIGHLIGHT:      This paper addresses the problem of improving the accuracy and consistency of responses to comparison questions by integrating logic rules and neural models.

53, TITLE:      When Autonomous Systems Meet Accuracy and Transferability through AI: A Survey
http://arxiv.org/abs/2003.12948
AUTHORS:        Chongzhen Zhang ; Jianrui Wang ; Gary G. Yen ; Chaoqiang Zhao ; Qiyu Sun ; Yang Tang ; Feng Qian ; Jürgen Kurths
HIGHLIGHT:      Here, we review the learning-based approaches in autonomous systems from the perspectives of accuracy and transferability.

54, TITLE:      A Unified MRC Framework for Named Entity Recognition
http://arxiv.org/abs/1910.11476
AUTHORS:        Xiaoya Li ; Jingrong Feng ; Yuxian Meng ; Qinghong Han ; Fei Wu ; Jiwei Li
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks.

55, TITLE:      When Person Re-identification Meets Changing Clothes
http://arxiv.org/abs/2003.04070
AUTHORS:        Fangbin Wan ; Yang Wu ; Xuelin Qian ; Yixiong Chen ; Yanwei Fu
COMMENTS:       Accepted by CVPRW 2020
HIGHLIGHT:      We find that changing clothes makes ReID a much harder problem in the sense of bringing difficulties to learning effective representations and also challenges the generalization ability of previous ReID models to identify persons with unseen (new) clothes.

56, TITLE:      Deep Shape from Polarization
http://arxiv.org/abs/1903.10210
AUTHORS:        Yunhao Ba ; Alex Ross Gilbert ; Franklin Wang ; Jinfa Yang ; Rui Chen ; Yiqin Wang ; Lei Yan ; Boxin Shi ; Achuta Kadambi
HIGHLIGHT:      This proposed approach achieves results that exceed the previous state-of-the-art on a challenging dataset we introduce.

57, TITLE:      DiResNet: Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images
http://arxiv.org/abs/2005.07232
AUTHORS:        Lei Ding ; Lorenzo Bruzzone
COMMENTS:       12 pages, 13 figures. To be submitted to IEEE TGRS
HIGHLIGHT:      In this paper, we consider the specific context of road extraction and present a direction-aware residual network (DiResNet) that includes three main contributions: 1) An asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology (DiResSeg); 2) A pixel-level supervision of local directions to enhance the embedding of linear features; 3) A refinement network to optimize the segmentation results (DiResRef).

58, TITLE:      HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
http://arxiv.org/abs/2005.10439
AUTHORS:        Kelei He ; Chunfeng Lian ; Bing Zhang ; Xin Zhang ; Xiaohuan Cao ; Dong Nie ; Yang Gao ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.

59, TITLE:      Deep Learning-Based Automated Image Segmentation for Concrete Petrographic Analysis
http://arxiv.org/abs/2005.10434
AUTHORS:        Yu Song ; Zilong Huang ; Chuanyue Shen ; Honghui Shi ; David A Lange
COMMENTS:       Accepted as a journal publication by Cement & Concrete Research
HIGHLIGHT:      In this study, we investigated the feasibility of using CNN to conduct concrete segmentation without the use of color treatment.

60, TITLE:      Adaptive Hierarchical Down-Sampling for Point Cloud Classification
http://arxiv.org/abs/1904.08506
AUTHORS:        Ehsan Nezhadarya ; Ehsan Taghavi ; Ryan Razani ; Bingbing Liu ; Jun Luo
HIGHLIGHT:      In this paper, we propose a permutation-invariant learning-based adaptive down-sampling layer, called Critical Points Layer (CPL), which reduces the number of points in an unordered point cloud while retaining the important points.

61, TITLE:      A Polymorphic RPC Calculus
http://arxiv.org/abs/1910.10988
AUTHORS:        Kwanghoon Choi ; James Cheney ; Simon Fowler ; Sam Lindley
COMMENTS:       SBMF-Brazilian Symposium on Formal Methods 2019
HIGHLIGHT:      This paper proposes a polymorphic RPC calculus to allow programmers to write succinct multi-tier programs using polymorphic location constructs.

62, 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.

63, TITLE:      Information-Theoretic Probing for Linguistic Structure
http://arxiv.org/abs/2004.03061
AUTHORS:        Tiago Pimentel ; Josef Valvoda ; Rowan Hall Maudslay ; Ran Zmigrod ; Adina Williams ; Ryan Cotterell
COMMENTS:       Accepted for publication at ACL 2020. This is the camera ready version. Code available in https://github.com/rycolab/info-theoretic-probing
HIGHLIGHT:      We propose an information-theoretic operationalization of probing as estimating mutual information that contradicts this received wisdom: one should always select the highest performing probe one can, even if it is more complex, since it will result in a tighter estimate, and thus reveal more of the linguistic information inherent in the representation.

64, TITLE:      Learning Sparse Low-Precision Neural Networks With Learnable Regularization
http://arxiv.org/abs/1809.00095
AUTHORS:        Yoojin Choi ; Mostafa El-Khamy ; Jungwon Lee
COMMENTS:       IEEE Access
HIGHLIGHT:      In particular, we propose using a learnable regularization coefficient with the MSQE regularizer to reinforce the convergence of high-precision weights to their quantized values.

65, TITLE:      Variance Loss in Variational Autoencoders
http://arxiv.org/abs/2002.09860
AUTHORS:        Andrea Asperti
COMMENTS:       Article accepted at the Sixth International Conference on Machine Learning, Optimization, and Data Science. July 19-23, 2020 - Certosa di Pontignano, Siena, Italy
HIGHLIGHT:      In this article, we highlight what appears to be major issue of Variational Autoencoders, evinced from an extensive experimentation with different network architectures and datasets: the variance of generated data is significantly lower than that of training data.

66, TITLE:      Enhancing Monotonic Multihead Attention for Streaming ASR
http://arxiv.org/abs/2005.09394
AUTHORS:        Hirofumi Inaguma ; Masato Mimura ; Tatsuya Kawahara
COMMENTS:       Corrected AISHELL-1 results
HIGHLIGHT:      To encourage every head to learn alignments properly, we propose HeadDrop regularization by masking out a part of heads stochastically during training.

67, TITLE:      Towards Solving Large-scale Expensive Optimization Problems Efficiently Using Coordinate Descent Algorithm
http://arxiv.org/abs/2003.03676
AUTHORS:        Shahryar Rahnamayan ; Seyed Jalaleddin Mousavirad ; Azam Asilian Bidgoli
HIGHLIGHT:      In this paper, we propose a modified Coordinate Descent algorithm (MCD) to tackle LSEGO problems with a limited computational budget.

68, TITLE:      EfficientDet: Scalable and Efficient Object Detection
http://arxiv.org/abs/1911.09070
AUTHORS:        Mingxing Tan ; Ruoming Pang ; Quoc V. Le
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we systematically study neural network architecture design choices for object detection and propose several key optimizations to improve efficiency.

69, TITLE:      Graph4Code: A Machine Interpretable Knowledge Graph for Code
http://arxiv.org/abs/2002.09440
AUTHORS:        Ibrahim Abdelaziz ; Julian Dolby ; James P. McCusker ; Kavitha Srinivas
COMMENTS:       Preprint, 16 pages
HIGHLIGHT:      We describe a set of generic extraction techniques that we applied to over 1.3M Python files drawn from GitHub, over 2,300 Python modules, as well as 47M forum posts to generate a graph with over 2 billion triples.

70, TITLE:      Pedestrian Detection with Wearable Cameras for the Blind: A Two-way Perspective
http://arxiv.org/abs/2003.12122
AUTHORS:        Kyungjun Lee ; Daisuke Sato ; Saki Asakawa ; Hernisa Kacorri ; Chieko Asakawa
COMMENTS:       The 2020 ACM CHI Conference on Human Factors in Computing Systems (CHI 2020)
HIGHLIGHT:      We explore this tension from both perspectives, those of sighted passersby and blind users, taking into account camera visibility, in-person versus remote experience, and extracted visual information.

71, TITLE:      Active, Continual Fine Tuning of Convolutional Neural Networks for Reducing Annotation Efforts
http://arxiv.org/abs/1802.00912
AUTHORS:        Zongwei Zhou ; Jae Y. Shin ; Suryakanth R. Gurudu ; Michael B. Gotway ; Jianming Liang
HIGHLIGHT:      To dramatically reduce annotation cost, this paper presents a novel method to naturally integrate active learning and transfer learning (fine-tuning) into a single framework, which starts directly with a pre-trained CNN to seek "worthy" samples for annotation and gradually enhances the (fine-tuned) CNN via continual fine-tuning.

72, TITLE:      Designing Normative Theories for Ethical and Legal Reasoning: LogiKEy Framework, Methodology, and Tool Support
http://arxiv.org/abs/1903.10187
AUTHORS:        Christoph Benzmüller ; Xavier Parent ; Leendert van der Torre
COMMENTS:       50 pages; 10 figures
HIGHLIGHT:      A framework and methodology---termed LogiKEy---for the design and engineering of ethical reasoners, normative theories and deontic logics is presented.

73, TITLE:      Ecological Semantics: Programming Environments for Situated Language Understanding
http://arxiv.org/abs/2003.04567
AUTHORS:        Ronen Tamari ; Gabriel Stanovsky ; Dafna Shahaf ; Reut Tsarfaty
COMMENTS:       Camera ready for Bridging AI and Cognitive Science (BAICS) workshop at ICLR2020. For interactive demos, see https://eco-sem.github.io/
HIGHLIGHT:      To this end we propose an environment-oriented ecological semantics, outlining theoretical and practical approaches towards implementation.

74, TITLE:      Efficient, Near Complete and Often Sound Hybrid Dynamic Data Race Prediction (extended version)
http://arxiv.org/abs/2004.06969
AUTHORS:        Martin Sulzmann ; Kai Stadtmüller
COMMENTS:       Some typos fixed, moved a few things between main and appendix
HIGHLIGHT:      We introduce an efficient, near complete and often sound dynamic data race prediction method that combines the lockset method with several improvements made in the area of happens-before methods. We provide extensive experimental data that shows that our method works well in practice.

75, TITLE:      A Systematic Assessment of Syntactic Generalization in Neural Language Models
http://arxiv.org/abs/2005.03692
AUTHORS:        Jennifer Hu ; Jon Gauthier ; Peng Qian ; Ethan Wilcox ; Roger P. Levy
COMMENTS:       To appear in the Proceedings of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      We present a systematic evaluation of the syntactic knowledge of neural language models, testing 20 combinations of model types and data sizes on a set of 34 English-language syntactic test suites.

76, TITLE:      Counting Query Answers over a DL-Lite Knowledge Base (extended version)
http://arxiv.org/abs/2005.05886
AUTHORS:        Diego Calvanese ; Julien Corman ; Davide Lanti ; Simon Razniewski
COMMENTS:       Extended version of an article to appear in the proceedings of IJCAI 2020
HIGHLIGHT:      In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration.

77, TITLE:      NegBERT: A Transfer Learning Approach for Negation Detection and Scope Resolution
http://arxiv.org/abs/1911.04211
AUTHORS:        Aditya Khandelwal ; Suraj Sawant
COMMENTS:       The 12th Language Resources and Evaluation Conference (LREC 2020)
HIGHLIGHT:      In this paper, we look at applying Transfer Learning to this problem.

78, TITLE:      Automatic Cost Function Learning with Interpretable Compositional Networks
http://arxiv.org/abs/2002.09811
AUTHORS:        Florian Richoux ; Jean-François Baffier
HIGHLIGHT:      Our method aims to learn cost functions in a supervised fashion, trying to reproduce the Hamming distance, by using a variation of neural networks we named Interpretable Compositional Networks, allowing us to get explainable results, unlike regular artificial neural networks.

79, TITLE:      PointHop++: A Lightweight Learning Model on Point Sets for 3D Classification
http://arxiv.org/abs/2002.03281
AUTHORS:        Min Zhang ; Yifan Wang ; Pranav Kadam ; Shan Liu ; C. -C. Jay Kuo
COMMENTS:       4pages, 4 figures
HIGHLIGHT:      The PointHop method was recently proposed by Zhang et al. for 3D point cloud classification with unsupervised feature extraction.

80, TITLE:      Dilated Point Convolutions: On the Receptive Field Size of Point Convolutions on 3D Point Clouds
http://arxiv.org/abs/1907.12046
AUTHORS:        Francis Engelmann ; Theodora Kontogianni ; Bastian Leibe
COMMENTS:       ICRA 2020 Video https://www.youtube.com/watch?v=JDfFmuOvMkM Project https://francisengelmann.github.io/DPC/
HIGHLIGHT:      In this work, we propose Dilated Point Convolutions (DPC).

81, TITLE:      Extremal Region Analysis based Deep Learning Framework for Detecting Defects
http://arxiv.org/abs/2003.08525
AUTHORS:        Zelin Deng ; Xiaolong Yan ; Shengjun Zhang ; Colleen P. Bailey
COMMENTS:       Unsatisfied with results
HIGHLIGHT:      A maximally stable extreme region (MSER) analysis based convolutional neural network (CNN) for unified defect detection framework is proposed in this paper.

82, TITLE:      Highway Transformer: Self-Gating Enhanced Self-Attentive Networks
http://arxiv.org/abs/2004.08178
AUTHORS:        Yekun Chai ; Jin Shuo ; Xinwen Hou
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      Through a pseudo information highway, we introduce a gated component self-dependency units (SDU) that incorporates LSTM-styled gating units to replenish internal semantic importance within the multi-dimensional latent space of individual representations.

83, TITLE:      Joint Detection and Tracking in Videos with Identification Features
http://arxiv.org/abs/2005.10905
AUTHORS:        Bharti Munjal ; Abdul Rafey Aftab ; Sikandar Amin ; Meltem D. Brandlmaier ; Federico Tombari ; Fabio Galasso
COMMENTS:       Accepted at Image and Vision Computing Journal
HIGHLIGHT:      Towards robust long-term tracking applicable to reduced-computational-power devices, we propose the first joint optimization of detection, tracking and re-identification features for videos.

84, TITLE:      Covering Codes using Insertions or Deletions
http://arxiv.org/abs/1911.09944
AUTHORS:        Andreas Lenz ; Cyrus Rashtchian ; Paul H. Siegel ; Eitan Yaakobi
COMMENTS:       13 pages
HIGHLIGHT:      We prove that codes exist with density that is only a factor $O(R \log R)$ larger than the lower bounds for all fixed~$R$.

85, TITLE:      PI-Net: A Deep Learning Approach to Extract Topological Persistence Images
http://arxiv.org/abs/1906.01769
AUTHORS:        Anirudh Som ; Hongjun Choi ; Karthikeyan Natesan Ramamurthy ; Matthew Buman ; Pavan Turaga
COMMENTS:       10 pages, 8 figures, 4 tables
HIGHLIGHT:      We take an important step in this paper to mitigate these bottlenecks by proposing a novel one-step approach to generate PIs directly from the input data.

86, TITLE:      RGBD-GAN: Unsupervised 3D Representation Learning From Natural Image Datasets via RGBD Image Synthesis
http://arxiv.org/abs/1909.12573
AUTHORS:        Atsuhiro Noguchi ; Tatsuya Harada
COMMENTS:       21 pages, ICLR2020
HIGHLIGHT:      We herein propose a novel generative model, RGBD-GAN, which achieves unsupervised 3D representation learning from 2D images.

87, TITLE:      A Novel Column Generation Heuristic for Airline Crew Pairing Optimization with Large-scale Complex Flight Networks
http://arxiv.org/abs/2005.08636
AUTHORS:        Divyam Aggarwal ; Dhish Kumar Saxena ; Thomas Bäck ; Michael Emmerich
COMMENTS:       23 pages, 7 figures, Manuscript to be submitted to a refereed journal
HIGHLIGHT:      To bridge the research-gap, this paper proposes a novel CG heuristic, which has enabled in-house development of an Airline Crew Pairing Optimizer (AirCROP ).

88, TITLE:      Real-Time Quality Assessment of Pediatric MRI via Semi-Supervised Deep Nonlocal Residual Neural Networks
http://arxiv.org/abs/1904.03639
AUTHORS:        Siyuan Liu ; Kim-Han Thung ; Weili Lin ; Pew-Thian Yap ; Dinggang Shen
HIGHLIGHT:      In this paper, we introduce an image quality assessment (IQA) method for pediatric T1- and T2-weighted MR images.

89, TITLE:      ESA: Entity Summarization with Attention
http://arxiv.org/abs/1905.10625
AUTHORS:        Dongjun Wei ; Yaxin Liu ; Fuqing Zhu ; Liangjun Zang ; Wei Zhou ; Jizhong Han ; Songlin Hu
COMMENTS:       12pages, accepted in EYRE@CIKM'2019
HIGHLIGHT:      In this paper we propose ESA, a neural network with supervised attention mechanisms for entity summarization.

90, TITLE:      A CNN-LSTM Architecture for Detection of Intracranial Hemorrhage on CT scans
http://arxiv.org/abs/2005.10992
AUTHORS:        Nhan T. Nguyen ; Dat Q. Tran ; Nghia T. Nguyen ; Ha Q. Nguyen
HIGHLIGHT:      We propose a novel method that combines a convolutional neural network (CNN) with a long short-term memory (LSTM) mechanism for accurate prediction of intracranial hemorrhage on computed tomography (CT) scans.

91, TITLE:      Baconian: A Unified Open-source Framework for Model-Based Reinforcement Learning
http://arxiv.org/abs/1904.10762
AUTHORS:        Linsen Dong ; Guanyu Gao ; Xinyi Zhang ; Liangyu Chen ; Yonggang Wen
HIGHLIGHT:      It has been widely adopted in the research of robotics, autonomous driving, etc.

92, TITLE:      FQuAD: French Question Answering Dataset
http://arxiv.org/abs/2002.06071
AUTHORS:        Martin d'Hoffschmidt ; Wacim Belblidia ; Tom Brendlé ; Quentin Heinrich ; Maxime Vidal
COMMENTS:       15 pages, 5 figures
HIGHLIGHT:      In the present work, we introduce the French Question Answering Dataset (FQuAD).

93, TITLE:      Incremental Sense Weight Training for the Interpretation of Contextualized Word Embeddings
http://arxiv.org/abs/1911.01623
AUTHORS:        Xinyi Jiang ; Zhengzhe Yang ; Jinho D. Choi
COMMENTS:       Accepted to AAAI Conference on Artificial Intelligence: Student Abstract and Poster Program, AAAI:SAP 2020
HIGHLIGHT:      We present a novel online algorithm that learns the essence of each dimension in word embeddings by minimizing the within-group distance of contextualized embedding groups.

94, TITLE:      Cross-Domain Few-Shot Learning with Meta Fine-Tuning
http://arxiv.org/abs/2005.10544
AUTHORS:        John Cai ; Sheng Mei Shen
COMMENTS:       4 Tables, 2 Figures
HIGHLIGHT:      In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark proposed by the CVPR 2020 Challenge.

95, TITLE:      Annotation-free Learning of Deep Representations for Word Spotting using Synthetic Data and Self Labeling
http://arxiv.org/abs/2003.01989
AUTHORS:        Fabian Wolf ; Gernot A. Fink
COMMENTS:       Accepted to Workshop on Document Analysis Systems (DAS) 2020
HIGHLIGHT:      In this work, we present an annotation-free method that still employs machine learning techniques and therefore outperforms other learning-free approaches.

96, TITLE:      Using context to make gas classifiers robust to sensor drift
http://arxiv.org/abs/2003.07292
AUTHORS:        J. Warner ; A. Devaraj ; R. Miikkulainen
HIGHLIGHT:      This paper shows how such a system can be defined without additional data acquisition by transfering knowledge from one time window to a subsequent one after drift has occurred.

97, TITLE:      Three-Filters-to-Normal: An Accurate and Ultrafast Surface Normal Estimator
http://arxiv.org/abs/2005.08165
AUTHORS:        Rui Fan ; Hengli Wang ; Bohuan Xue ; Huaiyang Huang ; Yuan Wang ; Ming Liu ; Ioannis Pitas
HIGHLIGHT:      This paper introduces an accurate and ultrafast SNE for structured range data. In our experiments, we created three large-scale synthetic datasets (easy, medium and hard) using 24 3-dimensional (3D) mesh models.

98, TITLE:      Efficient Image Gallery Representations at Scale Through Multi-Task Learning
http://arxiv.org/abs/2005.09027
AUTHORS:        Benjamin Gutelman ; Pavel Levin
COMMENTS:       To be published in Proceedings of the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval
HIGHLIGHT:      We study the problem of building a universal image gallery encoder through multi-task learning (MTL) approach and demonstrate that it is indeed a practical way to achieve generalizability of learned representations to new downstream tasks.

99, TITLE:      GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
http://arxiv.org/abs/2005.10497
AUTHORS:        Yonghyun Kim ; Wonpyo Park ; Myung-Cheol Roh ; Jongju Shin
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature.

100, TITLE:     Faster and Simpler SNN Simulation with Work Queues
http://arxiv.org/abs/1912.07423
AUTHORS:        Dennis Bautembach ; Iason Oikonomidis ; Nikolaos Kyriazis ; Antonis Argyros
COMMENTS:       Camera-ready version, as accepted by IJCNN 2020
HIGHLIGHT:      We present a clock-driven Spiking Neural Network simulator which is up to 3x faster than the state of the art while, at the same time, being more general and requiring less programming effort on both the user's and maintainer's side.

101, TITLE:     How much complexity does an RNN architecture need to learn syntax-sensitive dependencies?
http://arxiv.org/abs/2005.08199
AUTHORS:        Gantavya Bhatt ; Hritik Bansal ; Rishubh Singh ; Sumeet Agarwal
COMMENTS:       11 pages, 5 figures (including appendix); to appear at ACL SRW 2020
HIGHLIGHT:      In this paper, we seek to develop models that bridge the gap between biological plausibility and linguistic competence.

102, TITLE:     RHLE: Relational Reasoning for Existential Program Verification
http://arxiv.org/abs/2002.02904
AUTHORS:        Robert Dickerson ; Qianchuan Ye ; Benjamin Delaware
HIGHLIGHT:      This paper proposes a flexible way to underapproximate the behaviors of nondeterministic choices so that clients can soundly reason about the existence of desirable behaviors, while at the same time permitting some freedom in how those choices are implemented.

103, TITLE:     A Unified End-to-End Framework for Efficient Deep Image Compression
http://arxiv.org/abs/2002.03370
AUTHORS:        Jiaheng Liu ; Guo Lu ; Zhihao Hu ; Dong Xu
COMMENTS:       We will released our code and training data
HIGHLIGHT:      In this paper, we propose a unified framework called Efficient Deep Image Compression (EDIC) based on three new technologies, including a channel attention module, a Gaussian mixture model and a decoder-side enhancement module.

104, TITLE:     Conversational Question Answering over Passages by Leveraging Word Proximity Networks
http://arxiv.org/abs/2004.13117
AUTHORS:        Magdalena Kaiser ; Rishiraj Saha Roy ; Gerhard Weikum
COMMENTS:       SIGIR 2020 Demonstrations
HIGHLIGHT:      In this work, we demonstrate CROWN (Conversational passage ranking by Reasoning Over Word Networks): an unsupervised yet effective system for conversational QA with passage responses, that supports several modes of context propagation over multiple turns.

105, TITLE:     Causality-based Explanation of Classification Outcomes
http://arxiv.org/abs/2003.06868
AUTHORS:        Leopoldo Bertossi ; Jordan Li ; Maximilian Schleich ; Dan Suciu ; Zografoula Vagena
COMMENTS:       16 pages, 6 figures, 1 table
HIGHLIGHT:      We propose a simple definition of an explanation for the outcome of a classifier based on concepts from causality.

106, TITLE:     Proving P!=NP in first-order PA
http://arxiv.org/abs/2005.10080
AUTHORS:        Rupert McCallum
HIGHLIGHT:      We show that it is provable in PA that there is an arithmetically definable sequence $\{\phi_{n}:n \in \omega\}$ of $\Pi^{0}_{2}$-sentences, such that - PRA+$\{\phi_{n}:n \in \omega\}$ is $\Pi^{0}_{2}$-sound and $\Pi^{0}_{1}$-complete - the length of $\phi_{n}$ is bounded above by a polynomial function of $n$ with positive leading coefficient - PRA+$\phi_{n+1}$ always proves 1-consistency of PRA+$\phi_{n}$.

107, TITLE:     Learning Latent Causal Structures with a Redundant Input Neural Network
http://arxiv.org/abs/2003.13135
AUTHORS:        Jonathan D. Young ; Bryan Andrews ; Gregory F. Cooper ; Xinghua Lu
HIGHLIGHT:      In this paper, we address a problem for which it is known that inputs cause outputs, and these causal relationships are encoded by a causal network among a set of an unknown number of latent variables.

108, TITLE:     Federated Generative Adversarial Learning
http://arxiv.org/abs/2005.03793
AUTHORS:        Chenyou Fan ; Ping Liu
HIGHLIGHT:      To handle those problems, we propose a novel generative learning scheme utilizing a federated learning framework.

109, TITLE:     The Complexity of Helly-$B_{1}$ EPG Graph Recognition
http://arxiv.org/abs/1906.11185
AUTHORS:        Claudson F. Bornstein ; Martin Charles Golumbic ; Tanilson D. Santos ; Uéverton S. Souza ; Jayme L. Szwarcfiter
HIGHLIGHT:      In this paper, we show that given a graph $G$ and an integer $k$, the problem of determining whether $G$ admits a $B_k$-EPG representation whose edge-intersections of paths satisfy the Helly property, so-called Helly-$B_k$-EPG representation, is in NP, for every $k$ bounded by a polynomial function of $|V(G)|$.

110, TITLE:     Energy-Based Hindsight Experience Prioritization
http://arxiv.org/abs/1810.01363
AUTHORS:        Rui Zhao ; Volker Tresp
COMMENTS:       Published in Conference on Robot Learning (CoRL 2018) as oral presentation (7%), Zurich, Switzerland
HIGHLIGHT:      In this paper, we develop an energy-based framework for prioritizing hindsight experience in robotic manipulation tasks.

111, TITLE:     Efficient Dialog Policy Learning via Positive Memory Retention
http://arxiv.org/abs/1810.01371
AUTHORS:        Rui Zhao ; Volker Tresp
COMMENTS:       Published in IEEE Spoken Language Technology (SLT 2018), Athens, Greece
HIGHLIGHT:      To mitigate this problem, we describe an efficient policy gradient method using positive memory retention, which significantly increases the sample-efficiency.