2020.05.19.txt

==========New Papers==========
1, TITLE:       Large scale weakly and semi-supervised learning for low-resource video ASR
http://arxiv.org/abs/2005.07850
AUTHORS:        Kritika Singh ; Vimal Manohar ; Alex Xiao ; Sergey Edunov ; Ross Girshick ; Vitaliy Liptchinsky ; Christian Fuegen ; Yatharth Saraf ; Geoffrey Zweig ; Abdelrahman Mohamed
HIGHLIGHT:      We investigate distillation methods at the frame level and the sequence level for hybrid, encoder-only CTC-based, and encoder-decoder speech recognition systems on Dutch and Romanian languages using 27,000 and 58,000 hours of unlabeled audio respectively.

2, TITLE:       Neural Multi-Task Learning for Teacher Question Detection in Online Classrooms
http://arxiv.org/abs/2005.07845
AUTHORS:        Gale Yan Huang ; Jiahao Chen ; Haochen Liu ; Weiping Fu ; Wenbiao Ding ; Jiliang Tang ; Songfan Yang ; Guoliang Li ; Zitao Liu
COMMENTS:       The 21th International Conference on Artificial Intelligence in Education(AIED), 2020
HIGHLIGHT:      Therefore, in this work, we build an end-to-end neural framework that automatically detects questions from teachers' audio recordings.

3, TITLE:       COCAS: A Large-Scale Clothes Changing Person Dataset for Re-identification
http://arxiv.org/abs/2005.07862
AUTHORS:        Shijie Yu ; Shihua Li ; Dapeng Chen ; Rui Zhao ; Junjie Yan ; Yu Qiao
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      Based on COCAS, we introduce a new person re-id setting for clothes changing problem, where the query includes both a clothes template and a person image taking another clothes. To address the clothes changing person re-id problem, we construct a novel large-scale re-id benchmark named ClOthes ChAnging Person Set (COCAS), which provides multiple images of the same identity with different clothes.

4, TITLE:       Attribute2Font: Creating Fonts You Want From Attributes
http://arxiv.org/abs/2005.07865
AUTHORS:        Yizhi Wang ; Yue Gao ; Zhouhui Lian
COMMENTS:       SIGGRAPH 2020 techniqual paper; Wang and Gao contribute equally; Code: https://hologerry.github.io/Attr2Font/
HIGHLIGHT:      Inspired by this fact, we propose a novel model, Attribute2Font, to automatically create fonts by synthesizing visually-pleasing glyph images according to user-specified attributes and their corresponding values.

5, TITLE:       Partial Domain Adaptation Using Graph Convolutional Networks
http://arxiv.org/abs/2005.07858
AUTHORS:        Seunghan Yang ; Youngeun Kim ; Dongki Jung ; Changick Kim
HIGHLIGHT:      To overcome these problems, we propose a graph partial domain adaptation (GPDA) network, which exploits Graph Convolutional Networks for jointly considering data structure and the feature distribution of each class.

6, TITLE:       Concept Learning in Deep Reinforcement Learning
http://arxiv.org/abs/2005.07870
AUTHORS:        Diego Gomez ; Nicanor Quijano Silva ; Luis Felipe Giraldo
HIGHLIGHT:      Deep reinforcement learning techniques have shown to be a promising path to solve very complex tasks that once were thought to be out of the realm of machines.

7, TITLE:       MicroNet for Efficient Language Modeling
http://arxiv.org/abs/2005.07877
AUTHORS:        Zhongxia Yan ; Hanrui Wang ; Demi Guo ; Song Han
COMMENTS:       Accepted by PMLR
HIGHLIGHT:      In this paper, we provide the winning solution to the NeurIPS 2019 MicroNet Challenge in the language modeling track.

8, TITLE:       Integrating Semantic and Structural Information with Graph Convolutional Network for Controversy Detection
http://arxiv.org/abs/2005.07886
AUTHORS:        Lei Zhong ; Juan Cao ; Qiang Sheng ; Junbo Guo ; Ziang Wang
COMMENTS:       12 pages, 3 figures, 6 tables; To appear in ACL 2020 (long paper)
HIGHLIGHT:      To overcome the first two limitations, we propose Topic-Post-Comment Graph Convolutional Network (TPC-GCN), which integrates the information from the graph structure and content of topics, posts, and comments for post-level controversy detection.

9, TITLE:       Approaches to Improving Recognition of Underrepresented Named Entities in Hybrid ASR Systems
http://arxiv.org/abs/2005.08742
AUTHORS:        Tingzhi Mao ; Yerbolat Khassanov ; Van Tung Pham ; Haihua Xu ; Hao Huang ; Eng Siong Chng
HIGHLIGHT:      In this paper, we present a series of complementary approaches to improve the recognition of underrepresented named entities (NE) in hybrid ASR systems without compromising overall word error rate performance.

10, TITLE:      Multi-scale Grouped Dense Network for VVC Intra Coding
http://arxiv.org/abs/2005.07896
AUTHORS:        Xin Li ; Simeng Sun ; Zhizheng Zhang ; Zhibo Chen
HIGHLIGHT:      In this paper, we design the multi-scale grouped dense network (MSGDN) to further reduce the compression artifacts by combining the multi-scale and grouped dense block, which are integrated as the post-process network of VVC intra coding.

11, TITLE:      Glottal Source Estimation using an Automatic Chirp Decomposition
http://arxiv.org/abs/2005.07897
AUTHORS:        Thomas Drugman ; Baris Bozkurt ; Thierry Dutoit
HIGHLIGHT:      A method is proposed for determining automatically this contour by inspecting the root distribution.

12, TITLE:      Improving Named Entity Recognition in Tor Darknet with Local Distance Neighbor Feature
http://arxiv.org/abs/2005.08746
AUTHORS:        Mhd Wesam Al-Nabki ; Francisco Jañez-Martino ; Roberto A. Vasco-Carofilis ; Eduardo Fidalgo ; Javier Velasco-Mata
COMMENTS:       2 pages, 1 figure, to be published in conference JNIC 2020
HIGHLIGHT:      This paper adopts and improves the approach of Aguilar et al. by presenting a novel feature, called Local Distance Neighbor, which substitutes gazetteers.

13, 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.

14, TITLE:      Evaluating Performance of an Adult Pornography Classifier for Child Sexual Abuse Detection
http://arxiv.org/abs/2005.08766
AUTHORS:        Mhd Wesam Al-Nabki ; Eduardo Fidalgo ; Roberto A. Vasco-Carofilis ; Francisco Jañez-Martino ; Javier Velasco-Mata
COMMENTS:       4 pages, 8 figures, to be published in conference JNIC 2020
HIGHLIGHT:      In this paper, we identify which are the hardware and software requirements that may affect the performance of a forensic tool.

15, TITLE:      Adapting JPEG XS gains and priorities to tasks and contents
http://arxiv.org/abs/2005.08768
AUTHORS:        Benoit Brummer ; Christophe De Vleeschouwer
COMMENTS:       CLIC at CVPR 2020
HIGHLIGHT:      In this work we show that JPEG XS compression can be adapted to a specific given task and content, such as preserving visual quality on desktop content or maintaining high accuracy in neural network segmentation tasks, by optimizing its gain and priority parameters using the covariance matrix adaptation evolution strategy.

16, TITLE:      A Statistical Story of Visual Illusions
http://arxiv.org/abs/2005.08772
AUTHORS:        Elad Hirsch ; Ayellet Tal
HIGHLIGHT:      Given this tool, we present an approach that manages to support the paradigm and explain visual illusions in a unified manner.

17, TITLE:      Corpus of Chinese Dynastic Histories: Gender Analysis over Two Millennia
http://arxiv.org/abs/2005.08793
AUTHORS:        Sergey Zinin ; Yang Xu
COMMENTS:       12th Conference on Language Resources and Evaluation (LREC 2020), 9 pages, 7 tables
HIGHLIGHT:      This project introduces a new open-source corpus of twenty-four dynastic histories covered by Creative Commons license.

18, TITLE:      Causal Feature Learning for Utility-Maximizing Agents
http://arxiv.org/abs/2005.08792
AUTHORS:        David Kinney ; David Watson
HIGHLIGHT:      We propose a new technique, pragmatic causal feature learning (PCFL), which extends the original CFL algorithm in useful and intuitive ways.

19, TITLE:      TaBERT: Pretraining for Joint Understanding of Textual and Tabular Data
http://arxiv.org/abs/2005.08314
AUTHORS:        Pengcheng Yin ; Graham Neubig ; Wen-tau Yih ; Sebastian Riedel
COMMENTS:       To Appear at ACL 2020
HIGHLIGHT:      In this paper we present TaBERT, a pretrained LM that jointly learns representations for NL sentences and (semi-)structured tables.

20, TITLE:      AC-VRNN: Attentive Conditional-VRNN for Multi-Future Trajectory Prediction
http://arxiv.org/abs/2005.08307
AUTHORS:        Alessia Bertugli ; Simone Calderara ; Pasquale Coscia ; Lamberto Ballan ; Rita Cucchiara
HIGHLIGHT:      To this end, we propose a new generative model for multi-future trajectory prediction based on Conditional Variational Recurrent Neural Networks (C-VRNNs).

21, TITLE:      Context-Based Quotation Recommendation
http://arxiv.org/abs/2005.08319
AUTHORS:        Ansel MacLaughlin ; Tao Chen ; Burcu Karagol Ayan ; Dan Roth
COMMENTS:       11 pages, 2 figures
HIGHLIGHT:      We therefore propose a novel context-aware quote recommendation system which utilizes the content an author has already written to generate a ranked list of quotable paragraphs and spans of tokens from a given source document.

22, TITLE:      A Survey on Unknown Presentation Attack Detection for Fingerprint
http://arxiv.org/abs/2005.08337
AUTHORS:        Jag Mohan Singh ; Ahmed Madhun ; Guoqiang Li ; Raghavendra Ramachandra
COMMENTS:       Submitted to 3rd International Conference on Intelligent Technologies and Applications INTAP 2020
HIGHLIGHT:      In this survey paper, we present a comprehensive survey on existing PAD algorithms for fingerprint recognition systems, specifically from the standpoint of detecting unknown PAD.

23, TITLE:      Subject Identification Across Large Expression Variations Using 3D Facial Landmarks
http://arxiv.org/abs/2005.08339
AUTHORS:        Sk Rahatul Jannat ; Diego Fabiano ; Shaun Canavan ; Tempestt Neal
HIGHLIGHT:      Considering this, we propose to use 3D facial landmarks for the task of subject identification, over a range of expressed emotion.

24, TITLE:      Cross-Lingual Word Embeddings for Turkic Languages
http://arxiv.org/abs/2005.08340
AUTHORS:        Elmurod Kuriyozov ; Yerai Doval ; Carlos Gómez-Rodríguez
COMMENTS:       Final version, published in the proceedings of LREC 2020
HIGHLIGHT:      In this paper, we present the first viability study of established techniques to align monolingual embedding spaces for Turkish, Uzbek, Azeri, Kazakh and Kyrgyz, members of the Turkic family which is heavily affected by the low-resource constraint.

25, TITLE:      Impact of multiple modalities on emotion recognition: investigation into 3d facial landmarks, action units, and physiological data
http://arxiv.org/abs/2005.08341
AUTHORS:        Diego Fabiano ; Manikandan Jaishanker ; Shaun Canavan
HIGHLIGHT:      Considering this, we present an analysis of 3D facial data, action units, and physiological data as it relates to their impact on emotion recognition.

26, TITLE:      Detecting Forged Facial Videos using convolutional neural network
http://arxiv.org/abs/2005.08344
AUTHORS:        Neilesh Sambhu ; Shaun Canavan
HIGHLIGHT:      In this paper, we propose to detect forged videos, of faces, in online videos.

27, TITLE:      Facial Action Unit Detection using 3D Facial Landmarks
http://arxiv.org/abs/2005.08343
AUTHORS:        Saurabh Hinduja ; Shaun Canavan
HIGHLIGHT:      In this paper, we propose to detect facial action units (AU) using 3D facial landmarks.

28, 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
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.

29, TITLE:      Forecasting Solar Activity with Two Computational Intelligence Models (A Comparative Study)
http://arxiv.org/abs/2005.08350
AUTHORS:        M. Parsapoor ; U. Bilstrup ; B. Svensson
HIGHLIGHT:      Recently, we have proposed BELFIS (Brain Emotional Learning-based Fuzzy Inference System) as a tool for the forecasting of chaotic systems.

30, TITLE:      MixingBoard: a Knowledgeable Stylized Integrated Text Generation Platform
http://arxiv.org/abs/2005.08365
AUTHORS:        Xiang Gao ; Michel Galley ; Bill Dolan
COMMENTS:       accepted at ACL 2020
HIGHLIGHT:      We present MixingBoard, a platform for quickly building demos with a focus on knowledge grounded stylized text generation.

31, TITLE:      Multi-Objective level generator generation with Marahel
http://arxiv.org/abs/2005.08368
AUTHORS:        Ahmed Khalifa ; Julian Togelius
COMMENTS:       Submitted to PCGWorkshop 2020, 8pages, 7 figures
HIGHLIGHT:      This paper introduces a new system to design constructive level generators by searching the space of constructive level generators defined by Marahel language.

32, TITLE:      A Better Use of Audio-Visual Cues: Dense Video Captioning with Bi-modal Transformer
http://arxiv.org/abs/2005.08271
AUTHORS:        Vladimir Iashin ; Esa Rahtu
COMMENTS:       Project page is available on https://v-iashin.github.io/bmt
HIGHLIGHT:      In this paper, we introduce Bi-modal Transformer which generalizes the Transformer architecture for a bi-modal input.

33, TITLE:      Support-BERT: Predicting Quality of Question-Answer Pairs in MSDN using Deep Bidirectional Transformer
http://arxiv.org/abs/2005.08294
AUTHORS:        Bhaskar Sen ; Nikhil Gopal ; Xinwei Xue
HIGHLIGHT:      In this brief paper, we tackle the quality Q&A modeling problems from the community support websites using a recently developed deep learning model using bidirectional transformers.

34, TITLE:      Feature Fusion Strategies for End-to-End Evaluation of Cognitive Behavior Therapy Sessions
http://arxiv.org/abs/2005.07809
AUTHORS:        Zhuohao Chen ; Nikolaos Flemotomos ; Victor Ardulov ; Torrey A. Creed ; Zac E. Imel ; David C. Atkins ; Shrikanth Narayanan
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we develop an end-to-end pipeline that converts speech audio to diarized and transcribed text and extracts linguistic features to code the CBT sessions automatically.

35, TITLE:      KEIS@JUST at SemEval-2020 Task 12: Identifying Multilingual Offensive Tweets Using Weighted Ensemble and Fine-Tuned BERT
http://arxiv.org/abs/2005.07820
AUTHORS:        Saja Khaled Tawalbeh ; Mahmoud Hammad ; Mohammad AL-Smadi
COMMENTS:       8 pages without references, 4 figures, SemEval 2020 conference
HIGHLIGHT:      This research presents our team KEIS@JUST participation at SemEval-2020 Task 12 which represents shared task on multilingual offensive language.

36, TITLE:      Weakly Supervised Training of Hierarchical Attention Networks for Speaker Identification
http://arxiv.org/abs/2005.07817
AUTHORS:        Yanpei Shi ; Qiang Huang ; Thomas Hain
COMMENTS:       Submitted to Interspeech2020
HIGHLIGHT:      In this paper, a hierarchical attention network is proposed to solve a weakly labelled speaker identification problem.

37, TITLE:      Speaker Re-identification with Speaker Dependent Speech Enhancement
http://arxiv.org/abs/2005.07818
AUTHORS:        Yanpei Shi ; Qiang Huang ; Thomas Hain
COMMENTS:       Submitted to Interspeech2020
HIGHLIGHT:      This paper introduces a novel approach that cascades speech enhancement and speaker recognition.

38, TITLE:      Multi-step-ahead Prediction from Short-term Data by Delay-embedding-based Forecast Machine
http://arxiv.org/abs/2005.07842
AUTHORS:        Hao Peng ; Pei Chen ; Rui Liu
COMMENTS:       18 pages, 6 figures
HIGHLIGHT:      In this work, we proposed a novel framework, Delay-Embedding-based Forecast Machine (DEFM), to predict the future values of a target variable in an accurate and multi-step-ahead manner based on the high-dimensional short-term measurements.

39, TITLE:      Generalizing The Davenport-Mahler-Mignotte Bound -- The Weighted Case
http://arxiv.org/abs/2005.07843
AUTHORS:        Vikram Sharma
HIGHLIGHT:      In this paper, we generalize these results by allowing arbitrary positive integer weights on the edges of the graph, i.e., for a weight function $w: E \rightarrow \mathbb{Z}_{>0}$, we derive an amortized lower bound on $\prod_{(\alpha,\beta) \in E}|\alpha-\beta|^{w(\alpha,\beta)}$.

40, TITLE:      Joint Progressive Knowledge Distillation and Unsupervised Domain Adaptation
http://arxiv.org/abs/2005.07839
AUTHORS:        Le Thanh Nguyen-Meidine ; Eric Granger ; Madhu Kiran ; Jose Dolz ; Louis-Antoine Blais-Morin
COMMENTS:       Accepted to WCCI/IJCNN 2020
HIGHLIGHT:      In this paper, we propose an unexplored direction -- the joint optimization of CNNs to provide a compressed model that is adapted to perform well for a given target domain.

41, TITLE:      AccentDB: A Database of Non-Native English Accents to Assist Neural Speech Recognition
http://arxiv.org/abs/2005.07973
AUTHORS:        Afroz Ahamad ; Ankit Anand ; Pranesh Bhargava
COMMENTS:       Proceedings of the 12th Language Resources and Evaluation Conference - LREC, 2020
HIGHLIGHT:      Thus, our work aims to aid ASR systems at every stage of development with a database for training, classification models for feature augmentation, and neutralization systems for acoustic transformations of non-native accents of English.

42, TITLE:      Critical Impact of Social Networks Infodemic on Defeating Coronavirus COVID-19 Pandemic: Twitter-Based Study and Research Directions
http://arxiv.org/abs/2005.08820
AUTHORS:        Azzam Mourad ; Ali Srour ; Haidar Harmanani ; Cathia Jenainatiy ; Mohamad Arafeh
COMMENTS:       11 pages, 10 figures, Journal Article
HIGHLIGHT:      This paper presents a large-scale study based on data mined from Twitter.

43, TITLE:      Machine learning on Big Data from Twitter to understand public reactions to COVID-19
http://arxiv.org/abs/2005.08817
AUTHORS:        Jia Xue ; Junxiang Chen ; Chen Chen ; ChengDa Zheng ; Tingshao Zhu
HIGHLIGHT:      The study aims to understand Twitter users' discussions and reactions about the COVID-19.

44, TITLE:      Multi-level Feature Fusion-based CNN for Local Climate Zone Classification from Sentinel-2 Images: Benchmark Results on the So2Sat LCZ42 Dataset
http://arxiv.org/abs/2005.07983
AUTHORS:        Chunping Qiu ; Xiaochong Tong ; Michael Schmitt ; Benjamin Bechtel ; Xiao Xiang Zhu
HIGHLIGHT:      Using this base network, we propose fusing multi-level features using the extended Sen2LCZ-Net-MF.

45, TITLE:      Unsupervised Embedding-based Detection of Lexical Semantic Changes
http://arxiv.org/abs/2005.07979
AUTHORS:        Ehsaneddin Asgari ; Christoph Ringlstetter ; Hinrich Schütze
HIGHLIGHT:      This paper describes EmbLexChange, a system introduced by the "Life-Language" team for SemEval-2020 Task 1, on unsupervised detection of lexical-semantic changes.

46, TITLE:      Inflecting when there's no majority: Limitations of encoder-decoder neural networks as cognitive models for German plurals
http://arxiv.org/abs/2005.08826
AUTHORS:        Kate McCurdy ; Sharon Goldwater ; Adam Lopez
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      Encoder-decoder models do generalize the most frequently produced plural class, but do not show human-like variability or 'regular' extension of these other plural markers. To investigate this question, we first collect a new dataset from German speakers (production and ratings of plural forms for novel nouns) that is designed to avoid sources of information unavailable to the ED model.

47, TITLE:      Visual Memorability for Robotic Interestingness Prediction via Unsupervised Online Learning
http://arxiv.org/abs/2005.08829
AUTHORS:        Chen Wang ; Wenshan Wang ; Yuheng Qiu ; Yafei Hu ; Sebastian Scherer
HIGHLIGHT:      In this paper, we aim to solve the problem of interesting scene prediction for mobile robots.

48, TITLE:      Non-Linearities Improve OrigiNet based on Active Imaging for Micro Expression Recognition
http://arxiv.org/abs/2005.07991
AUTHORS:        Monu Verma ; Santosh Kumar Vipparthi ; Girdhari Singh
HIGHLIGHT:      In this paper, we propose a new refined rectified linear unit (RReLU), which overcome the problem of vanishing gradient and dying ReLU.

49, TITLE:      Revisiting Agglomerative Clustering
http://arxiv.org/abs/2005.07995
AUTHORS:        Eric K. Tokuda ; Cesar H. Comin ; Luciano da F. Costa
HIGHLIGHT:      More importantly, we adopt a generic model of clusters involving a higher density core surrounded by a transition zone, followed by a sparser set of outliers.

50, TITLE:      A Text Reassembling Approach to NaturalLanguage Generation
http://arxiv.org/abs/2005.07988
AUTHORS:        Xiao Li ; Kees van Deemter ; Chenghua Lin
HIGHLIGHT:      Focussing on some of the key NLG tasks (namely Content Selection, Lexical Choice, and Linguistic Realisation), we propose a novel approach, called the Text Reassembling approach to NLG (TRG), which approaches the ideal of a purely statistical approach very closely, and which is at the same time highly transparent.

51, TITLE:      MMFashion: An Open-Source Toolbox for Visual Fashion Analysis
http://arxiv.org/abs/2005.08847
AUTHORS:        Xin Liu ; Jiancheng Li ; Jiaqi Wang ; Ziwei Liu
HIGHLIGHT:      We welcome all contributions to this still-growing efforts towards open science: https://github.com/open-mmlab/mmfashion.

52, TITLE:      Grammatical gender associations outweigh topical gender bias in crosslinguistic word embeddings
http://arxiv.org/abs/2005.08864
AUTHORS:        Katherine McCurdy ; Oguz Serbetci
COMMENTS:       Extended abstract presented at the WiNLP workshop, ACL 2017
HIGHLIGHT:      Recent research has demonstrated that vector space models of semantics can reflect undesirable biases in human culture.

53, TITLE:      Span-ConveRT: Few-shot Span Extraction for Dialog with Pretrained Conversational Representations
http://arxiv.org/abs/2005.08866
AUTHORS:        Sam Coope ; Tyler Farghly ; Daniela Gerz ; Ivan Vulić ; Matthew Henderson
COMMENTS:       ACL 2020
HIGHLIGHT:      We introduce Span-ConveRT, a light-weight model for dialog slot-filling which frames the task as a turn-based span extraction task.

54, TITLE:      Local and Global Explanations of Agent Behavior: Integrating Strategy Summaries with Saliency Maps
http://arxiv.org/abs/2005.08874
AUTHORS:        Tobias Huber ; Katharina Weitz ; Elisabeth André ; Ofra Amir
HIGHLIGHT:      In this paper, we combine global and local explanation methods, and evaluate their joint and separate contributions, providing (to the best of our knowledge) the first user study of combined local and global explanations for RL agents.

55, TITLE:      Deep Implicit Volume Compression
http://arxiv.org/abs/2005.08877
AUTHORS:        Danhang Tang ; Saurabh Singh ; Philip A. Chou ; Christian Haene ; Mingsong Dou ; Sean Fanello ; Jonathan Taylor ; Philip Davidson ; Onur G. Guleryuz ; Yinda Zhang ; Shahram Izadi ; Andrea Tagliasacchi ; Sofien Bouaziz ; Cem Keskin
COMMENTS:       Danhang Tang and Saurabh Singh have equal contribution
HIGHLIGHT:      We describe a novel approach for compressing truncated signed distance fields (TSDF) stored in 3D voxel grids, and their corresponding textures.

56, TITLE:      Content analysis of Persian/Farsi Tweets during COVID-19 pandemic in Iran using NLP
http://arxiv.org/abs/2005.08400
AUTHORS:        Pedram Hosseini ; Poorya Hosseini ; David A. Broniatowski
HIGHLIGHT:      In this study, using more than 530,000 original tweets in Persian/Farsi on COVID-19, we analyzed the topics discussed among users, who are mainly Iranians, to gauge and track the response to the pandemic and how it evolved over time.

57, TITLE:      The Weifeiler-Leman Algorithm and Recognition of Graph Properties
http://arxiv.org/abs/2005.08887
AUTHORS:        Frank Fuhlbrück ; Johannes Köbler ; Ilia Ponomarenko ; Oleg Verbitsky
COMMENTS:       24 pages, 2 figures. This paper supersedes Section 5 in the first version of arXiv:2002.04590
HIGHLIGHT:      We address the applicability of $k$-WL to recognition of graph properties.

58, TITLE:      Deep Snow: Synthesizing Remote Sensing Imagery with Generative Adversarial Nets
http://arxiv.org/abs/2005.08892
AUTHORS:        Christopher X. Ren ; Amanda Ziemann ; James Theiler ; Alice M. S. Durieux
HIGHLIGHT:      In this work we demonstrate that generative adversarial networks (GANs) can be used to generate realistic pervasive changes in remote sensing imagery, even in an unpaired training setting.

59, TITLE:      Generative Tweening: Long-term Inbetweening of 3D Human Motions
http://arxiv.org/abs/2005.08891
AUTHORS:        Yi Zhou ; Jingwan Lu ; Connelly Barnes ; Jimei Yang ; Sitao Xiang ; Hao li
HIGHLIGHT:      To this end, we introduce the problem of long-term inbetweening, which involves automatically synthesizing complex motions over a long time interval given very sparse keyframes by users.

60, TITLE:      Single-sample writers -- "Document Filter" and their impacts on writer identification
http://arxiv.org/abs/2005.08424
AUTHORS:        Fabio Pinhelli ; Alceu S. Britto Jr ; Luiz S. Oliveira ; Yandre M. G. Costa ; Diego Bertolini
HIGHLIGHT:      In this work, perform a detailed study in which we dissect whether or not the use of a database with only a single sample taken from some writers may skew the results obtained in the experimental protocol.

61, TITLE:      Syntax-guided Controlled Generation of Paraphrases
http://arxiv.org/abs/2005.08417
AUTHORS:        Ashutosh Kumar ; Kabir Ahuja ; Raghuram Vadapalli ; Partha Talukdar
COMMENTS:       16 pages, 3 figures, Accepted to TACL 2020
HIGHLIGHT:      We address this limitation in the paper and propose Syntax Guided Controlled Paraphraser (SGCP), an end-to-end framework for syntactic paraphrase generation.

62, TITLE:      Deep Learning and Bayesian Deep Learning Based Gender Prediction in Multi-Scale Brain Functional Connectivity
http://arxiv.org/abs/2005.08431
AUTHORS:        Gengyan Zhao ; Gyujoon Hwang ; Cole J. Cook ; Fang Liu ; Mary E. Meyerand ; Rasmus M. Birn
COMMENTS:       40 pages, 10 figures
HIGHLIGHT:      Hence, in this study we propose to predict gender from multiple scales of brain FC with deep learning, which can extract full FC patterns as features.

63, TITLE:      The NTNU System at the Interspeech 2020 Non-Native Children's Speech ASR Challenge
http://arxiv.org/abs/2005.08433
AUTHORS:        Tien-Hong Lo ; Fu-An Chao ; Shi-Yan Weng ; Berlin Chen
COMMENTS:       Submitted to Interspeech 2020 Special Session: Shared Task on Automatic Speech Recognition for Non-Native Children's Speech
HIGHLIGHT:      This paper describes the NTNU ASR system participating in the Interspeech 2020 Non-Native Children's Speech ASR Challenge supported by the SIG-CHILD group of ISCA.

64, TITLE:      An Effective End-to-End Modeling Approach for Mispronunciation Detection
http://arxiv.org/abs/2005.08440
AUTHORS:        Tien-Hong Lo ; Shi-Yan Weng ; Hsiu-Jui Chang ; Berlin Chen
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      Despite the widespread adoption of E2E modeling frameworks on ASR, there still is a dearth of work on investigating the E2E frameworks for use in computer-assisted pronunciation learning (CAPT), particularly for Mispronunciation detection (MD).

65, TITLE:      Cross-Task Transfer for Multimodal Aerial Scene Recognition
http://arxiv.org/abs/2005.08449
AUTHORS:        Di Hu ; Xuhong Li ; Lichao Mou ; Pu Jin ; Dong Chen ; Liping Jing ; Xiaoxiang Zhu ; Dejing Dou
HIGHLIGHT:      In this paper, for improving the performance on the aerial scene recognition, we explore a novel audiovisual aerial scene recognition task using both images and sounds as input. For this purpose, we have constructed a new dataset named AuDio Visual Aerial sceNe reCognition datasEt (ADVANCE).

66, TITLE:      Deep Convolutional Sparse Coding Networks for Image Fusion
http://arxiv.org/abs/2005.08448
AUTHORS:        Shuang Xu ; Zixiang Zhao ; Yicheng Wang ; Chunxia Zhang ; Junmin Liu ; Jiangshe Zhang
HIGHLIGHT:      This paper presents three deep convolutional sparse coding (CSC) networks for three kinds of image fusion tasks (i.e., infrared and visible image fusion, multi-exposure image fusion, and multi-modal image fusion).

67, TITLE:      Large-Scale Object Detection in the Wild from Imbalanced Multi-Labels
http://arxiv.org/abs/2005.08455
AUTHORS:        Junran Peng ; Xingyuan Bu ; Ming Sun ; Zhaoxiang Zhang ; Tieniu Tan ; Junjie Yan
COMMENTS:       CVPR2020 oral. The first two authors contribute equally
HIGHLIGHT:      In this work, we quantitatively analyze these label problems and provide a simple but effective solution.

68, TITLE:      Bayesian convolutional neural network based MRI brain extraction on nonhuman primates
http://arxiv.org/abs/2005.08460
AUTHORS:        Gengyan Zhao ; Fang Liu ; Jonathan A. Oler ; Mary E. Meyerand ; Ned H. Kalin ; Rasmus M. Birn
COMMENTS:       37 pages, 14 figures
HIGHLIGHT:      To overcome the challenges of brain extraction in nonhuman primates, we propose a fully-automated brain extraction pipeline combining deep Bayesian convolutional neural network (CNN) and fully connected three-dimensional (3D) conditional random field (CRF).

69, TITLE:      Feature Transformation Ensemble Model with Batch Spectral Regularization for Cross-Domain Few-Shot Classification
http://arxiv.org/abs/2005.08463
AUTHORS:        Bingyu Liu ; Zhen Zhao ; Zhenpeng Li ; Jianan Jiang ; Yuhong Guo ; Haifeng Shen ; Jieping Ye
HIGHLIGHT:      In this paper, we propose a feature transformation ensemble model with batch spectral regularization and label propagation for the CD-FSL challenge.

70, TITLE:      Context-aware and Scale-insensitive Temporal Repetition Counting
http://arxiv.org/abs/2005.08465
AUTHORS:        Huaidong Zhang ; Xuemiao Xu ; Guoqiang Han ; Shengfeng He
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we tailor a context-aware and scale-insensitive framework, to tackle the challenges in repetition counting caused by the unknown and diverse cycle-lengths. To benefit the training and evaluation of temporal repetition counting area, we construct a new and largest benchmark, which contains 526 videos with diverse repetitive actions.

71, TITLE:      Text Classification with Few Examples using Controlled Generalization
http://arxiv.org/abs/2005.08469
AUTHORS:        Abhijit Mahabal ; Jason Baldridge ; Burcu Karagol Ayan ; Vincent Perot ; Dan Roth
HIGHLIGHT:      This produces task-specific semantic vectors; here, we show that a feed-forward network over these vectors is especially effective in low-data scenarios, compared to existing state-of-the-art methods.

72, TITLE:      Extreme Low-Light Imaging with Multi-granulation Cooperative Networks
http://arxiv.org/abs/2005.08001
AUTHORS:        Keqi Wang ; Peng Gao ; Steven Hoi ; Qian Guo ; Yuhua Qian
HIGHLIGHT:      In this paper, we propose a novel method of multi-granulation cooperative networks (MCN) with bidirectional information flow to enhance extreme low-light images, and design an illumination map estimation function (IMEF) to preserve high dynamic range (HDR).

73, TITLE:      Deep Lighting Environment Map Estimation from Spherical Panoramas
http://arxiv.org/abs/2005.08000
AUTHORS:        Vasileios Gkitsas ; Nikolaos Zioulis ; Federico Alvarez ; Dimitrios Zarpalas ; Petros Daras
COMMENTS:       Code and models available at https://vcl3d.github.io/DeepPanoramaLighting
HIGHLIGHT:      In this work we present a data-driven model that estimates an HDR lighting environment map from a single LDR monocular spherical panorama.

74, TITLE:      Towards in-store multi-person tracking using head detection and track heatmaps
http://arxiv.org/abs/2005.08009
AUTHORS:        Aibek Musaev ; Jiangping Wang ; Liang Zhu ; Cheng Li ; Yi Chen ; Jialin Liu ; Wanqi Zhang ; Juan Mei ; De Wang
HIGHLIGHT:      In this paper, we study the problem of computer vision based customer tracking in retail industry. To this end, we introduce a dataset collected from a camera in an office environment where participants mimic various behaviors of customers in a supermarket.

75, TITLE:      Attention-based Transducer for Online Speech Recognition
http://arxiv.org/abs/2005.08497
AUTHORS:        Bin Wang ; Yan Yin ; Hui Lin
COMMENTS:       submitted to Interspeech 2020
HIGHLIGHT:      We propose attention-based transducer with modification over RNN-T in two aspects.

76, TITLE:      Fixed Point Semantics for Stream Reasoning
http://arxiv.org/abs/2005.08384
AUTHORS:        Christian Antić
HIGHLIGHT:      This paper fixes all of the aforementioned shortcomings of LARS.

77, TITLE:      Vector-Quantized Autoregressive Predictive Coding
http://arxiv.org/abs/2005.08392
AUTHORS:        Yu-An Chung ; Hao Tang ; James Glass
HIGHLIGHT:      In this work, we propose Vector-Quantized Autoregressive Predictive Coding (VQ-APC), a novel model that produces quantized representations, allowing us to explicitly control the amount of information encoded in the representations.

78, TITLE:      A tutorial introduction to quantum circuit programming in dependently typed Proto-Quipper
http://arxiv.org/abs/2005.08396
AUTHORS:        Peng Fu ; Kohei Kishida ; Neil J. Ross ; Peter Selinger
COMMENTS:       To appear in Proceedings of the 12th International Conference on Reversible Computation (RC 2020), Oslo, Norway, 2020
HIGHLIGHT:      We introduce dependently typed Proto-Quipper, or Proto-Quipper-D for short, an experimental quantum circuit programming language with linear dependent types.

79, TITLE:      T-VSE: Transformer-Based Visual Semantic Embedding
http://arxiv.org/abs/2005.08399
AUTHORS:        Muhammet Bastan ; Arnau Ramisa ; Mehmet Tek
COMMENTS:       To appear: CVPR 2020 Workshop on Computer Vision for Fashion, Art and Design (CVFAD 2020)
HIGHLIGHT:      In this paper, we show that dataset scale and training strategy are critical and demonstrate that transformer-based cross-modal embeddings outperform word average and RNN-based embeddings by a large margin, when trained on a large dataset of e-commerce product image-title pairs.

80, TITLE:      Oscillating Statistical Moments for Speech Polarity Detection
http://arxiv.org/abs/2005.07901
AUTHORS:        Thomas Drugman ; Thierry Dutoit
HIGHLIGHT:      This paper proposes a new approach of polarity detection relying on oscillating statistical moments.

81, TITLE:      The Power of Triply Complementary Priors for Image Compressive Sensing
http://arxiv.org/abs/2005.07902
AUTHORS:        Zhiyuan Zha ; Xin Yuan ; Joey Tianyi Zhou ; Jiantao Zhou ; Bihan Wen ; Ce Zhu
HIGHLIGHT:      In this paper, we propose a joint low-rank and deep (LRD) image model, which contains a pair of triply complementary priors, namely \textit{external} and \textit{internal}, \textit{deep} and \textit{shallow}, and \textit{local} and \textit{non-local} priors.

82, TITLE:      Spike-Triggered Non-Autoregressive Transformer for End-to-End Speech Recognition
http://arxiv.org/abs/2005.07903
AUTHORS:        Zhengkun Tian ; Jiangyan Yi ; Jianhua Tao ; Ye Bai ; Shuai Zhang ; Zhengqi Wen
COMMENTS:       5 pages
HIGHLIGHT:      To address this problem and improve the inference speed, we propose a spike-triggered non-autoregressive transformer model for end-to-end speech recognition, which introduces a CTC module to predict the length of the target sequence and accelerate the convergence.

83, TITLE:      A Dichotomy for Real Boolean Holant Problems
http://arxiv.org/abs/2005.07906
AUTHORS:        Shuai Shao ; Jin-Yi Cai
COMMENTS:       91 pages, 4 figures
HIGHLIGHT:      We prove a complexity dichotomy for Holant problems on the boolean domain with arbitrary sets of real-valued constraint functions.

84, TITLE:      Reducing Spelling Inconsistencies in Code-Switching ASR using Contextualized CTC Loss
http://arxiv.org/abs/2005.07920
AUTHORS:        Burin Naowarat ; Thananchai Kongthaworn ; Korrawe Karunratanakul ; Sheng Hui Wu ; Ekapol Chuangsuwanich
COMMENTS:       7 pages, 5 figures, submitted to INTERSPEECH 2020
HIGHLIGHT:      We propose Contextualized Connectionist Temporal Classification (CCTC) loss to encourage spelling consistencies of a character-based non-autoregressive ASR which allows for faster inference.

85, TITLE:      Deep-learning of Parametric Partial Differential Equations from Sparse and Noisy Data
http://arxiv.org/abs/2005.07916
AUTHORS:        Hao Xu ; Dongxiao Zhang ; Junsheng Zeng
COMMENTS:       30 pages, 6 figures, and 7 tables
HIGHLIGHT:      In this work, a new framework, which combines neural network, genetic algorithm and adaptive methods, is put forward to address all of these challenges simultaneously.

86, TITLE:      Deep feature fusion for self-supervised monocular depth prediction
http://arxiv.org/abs/2005.07922
AUTHORS:        Vinay Kaushik ; Brejesh Lall
COMMENTS:       4 pages, 2 Tables, 2 Figures
HIGHLIGHT:      We propose a deep feature fusion method utilising features at multiple scales for learning self-supervised depth from scratch.

87, TITLE:      Sequential Sentence Matching Network for Multi-turn Response Selection in Retrieval-based Chatbots
http://arxiv.org/abs/2005.07923
AUTHORS:        Chao Xiong ; Che Liu ; Zijun Xu ; Junfeng Jiang ; Jieping Ye
COMMENTS:       10 pages, 4 figures
HIGHLIGHT:      In this work, we propose a matching network, called sequential sentence matching network (S2M), to use the sentence-level semantic information to address the problem.

88, TITLE:      Artificial Intelligence Assisted Collaborative Edge Caching in Small Cell Networks
http://arxiv.org/abs/2005.07941
AUTHORS:        Md Ferdous Pervej ; Le Thanh Tan ; Rose Qingyang Hu
COMMENTS:       Submitted for possible publication
HIGHLIGHT:      Thanks to artificial intelligence (AI), based on the methodologies of the conventional particle swarm optimization (PSO), we propose a modified PSO (M-PSO) to efficiently solve the complex constraint problem in a reasonable time.

89, TITLE:      ApplicaAI at SemEval-2020 Task 11: On RoBERTa-CRF, Span CLS and Whether Self-Training Helps Them
http://arxiv.org/abs/2005.07934
AUTHORS:        Dawid Jurkiewicz ; Łukasz Borchmann ; Izabela Kosmala ; Filip Graliński
HIGHLIGHT:      An ensemble of RoBERTa-based models was proposed for the TC task, with one of them making use of Span CLS layers we introduce in the present paper.

90, TITLE:      Logical Inferences with Comparatives and Generalized Quantifiers
http://arxiv.org/abs/2005.07954
AUTHORS:        Izumi Haruta ; Koji Mineshima ; Daisuke Bekki
COMMENTS:       To appear in the Proceedings of the Association for Computational Linguistics: Student Research Workshop (ACL-SRW 2020)
HIGHLIGHT:      In this paper, we present a compositional semantics that maps various comparative constructions in English to semantic representations via Combinatory Categorial Grammar (CCG) parsers and combine it with an inference system based on automated theorem proving.

91, TITLE:      Polynomial-time approximation algorithms for the antiferromagnetic Ising model on line graphs
http://arxiv.org/abs/2005.07944
AUTHORS:        Martin Dyer ; Marc Heinrich ; Mark Jerrum ; Haiko Müller
COMMENTS:       17 pages
HIGHLIGHT:      We present a polynomial-time Markov chain Monte Carlo algorithm for estimating the partition function of the antiferromagnetic Ising model on any line graph.

92, TITLE:      Data Driven Aircraft Trajectory Prediction with Deep Imitation Learning
http://arxiv.org/abs/2005.07960
AUTHORS:        Alevizos Bastas ; Theocharis Kravaris ; George A. Vouros
HIGHLIGHT:      In this paper we approach the data-driven trajectory prediction problem as an imitation learning task, where we aim to imitate experts "shaping" the trajectory.

93, TITLE:      Hierarchical and Efficient Learning for Person Re-Identification
http://arxiv.org/abs/2005.08812
AUTHORS:        Jiangning Zhang ; Liang Liu ; Chao Xu ; Yong Liu
HIGHLIGHT:      In this paper, we propose a novel Hierarchical and Efficient Network (HENet) that learns hierarchical global, partial, and recovery features ensemble under the supervision of multiple loss combinations.

94, TITLE:      Interaction Matching for Long-Tail Multi-Label Classification
http://arxiv.org/abs/2005.08805
AUTHORS:        Sean MacAvaney ; Franck Dernoncourt ; Walter Chang ; Nazli Goharian ; Ophir Frieder
HIGHLIGHT:      We present an elegant and effective approach for addressing limitations in existing multi-label classification models by incorporating interaction matching, a concept shown to be useful for ad-hoc search result ranking.

95, TITLE:      Niose-Sampling Cross Entropy Loss: Improving Disparity Regression Via Cost Volume Aware Regularizer
http://arxiv.org/abs/2005.08806
AUTHORS:        Yang Chen ; Zongqing Lu ; Xuechen Zhang ; Lei Chen ; Qinming Liao
COMMENTS:       Accepted by IEEE ICIP 2020
HIGHLIGHT:      In this paper, inspired by previous canonical definition of cost volume, we propose the noise-sampling cross entropy loss function to regularize the cost volume produced by deep neural networks to be unimodal and coherent.

96, TITLE:      VecQ: Minimal Loss DNN Model Compression With Vectorized Weight Quantization
http://arxiv.org/abs/2005.08501
AUTHORS:        Cheng Gong ; Yao Chen ; Ye Lu ; Tao Li ; Cong Hao ; Deming Chen
COMMENTS:       14 pages, 9 figures, Journal
HIGHLIGHT:      In this paper, we propose a novel metric called Vector Loss.

97, TITLE:      Spatio-Temporal Graph Transformer Networks for Pedestrian Trajectory Prediction
http://arxiv.org/abs/2005.08514
AUTHORS:        Cunjun Yu ; Xiao Ma ; Jiawei Ren ; Haiyu Zhao ; Shuai Yi
COMMENTS:       19 pages, 8 figures, 2 tables
HIGHLIGHT:      In this paper, we present STAR, a Spatio-Temporal grAph tRansformer framework, which tackles trajectory prediction by only attention mechanisms.

98, TITLE:      Robust Training of Vector Quantized Bottleneck Models
http://arxiv.org/abs/2005.08520
AUTHORS:        Adrian Łańcucki ; Jan Chorowski ; Guillaume Sanchez ; Ricard Marxer ; Nanxin Chen ; Hans J. G. A. Dolfing ; Sameer Khurana ; Tanel Alumäe ; Antoine Laurent
COMMENTS:       Published at IJCNN 2020
HIGHLIGHT:      In this paper we demonstrate methods for reliable and efficient training of discrete representation using Vector-Quantized Variational Auto-Encoder models (VQ-VAEs).

99, TITLE:      Automatic Knowledge Acquisition for Object-Oriented Expert Systems
http://arxiv.org/abs/2005.08517
AUTHORS:        Joël Colloc ; Danielle Boulanger
HIGHLIGHT:      We describe an Object Oriented Model for building Expert Systems.

100, TITLE:     Towards Question Format Independent Numerical Reasoning: A Set of Prerequisite Tasks
http://arxiv.org/abs/2005.08516
AUTHORS:        Swaroop Mishra ; Arindam Mitra ; Neeraj Varshney ; Bhavdeep Sachdeva ; Chitta Baral
COMMENTS:       10 pages
HIGHLIGHT:      In pursuit of this goal, we introduce NUMBERGAME, a multifaceted benchmark to evaluate model performance across numerical reasoning tasks of eight diverse formats.

101, TITLE:     SemEval-2020 Task 5: Detecting Counterfactuals by Disambiguation
http://arxiv.org/abs/2005.08519
AUTHORS:        Hanna Abi Akl ; Dominique Mariko ; Estelle Labidurie
HIGHLIGHT:      In this paper, we explore strategies to detect and evaluate counterfactual sentences.

102, TITLE:     An Algebraic Model For Quorum Systems
http://arxiv.org/abs/2005.08536
AUTHORS:        Alex Pellegrini ; Luca Zanolini
COMMENTS:       15 pages, 3 algorithms
HIGHLIGHT:      In this paper we give a new interpretation of quorum systems, starting with classical majority-based quorum systems and extending this to Byzantine quorum systems.

103, TITLE:     Omni-supervised Facial Expression Recognition: A Simple Baseline
http://arxiv.org/abs/2005.08551
AUTHORS:        Ping Liu ; Yunchao Wei ; Zibo Meng ; Weihong Deng ; Joey Tianyi Zhou ; Yi Yang
HIGHLIGHT:      In this paper, we target on advancing the performance in facial expression recognition (FER) by exploiting omni-supervised learning.

104, TITLE:     Learning to Model and Calibrate Optics via a Differentiable Wave Optics Simulator
http://arxiv.org/abs/2005.08562
AUTHORS:        Josue Page ; Paolo Favaro
COMMENTS:       6 pages, 3 figures, for source code see https://github.com/pvjosue/WaveBlocks, to be published in IEEE 2020 International Conference on Image Processing (ICIP 2020)
HIGHLIGHT:      We present a novel learning-based method to build a differentiable computational model of a real fluorescence microscope.

105, TITLE:     Audio-visual Multi-channel Recognition of Overlapped Speech
http://arxiv.org/abs/2005.08571
AUTHORS:        Jianwei Yu ; Bo Wu ; Rongzhi Gu Shi-Xiong Zhang Lianwu Chen Yong Xu Meng Yu ; Dan Su ; Dong Yu ; Xunying Liu ; Helen Meng
COMMENTS:       submitted to Interspeech 2020
HIGHLIGHT:      Motivated by the invariance of visual modality to acoustic signal corruption, this paper presents an audio-visual multi-channel overlapped speech recognition system featuring tightly integrated separation front-end and recognition back-end.

106, TITLE:     Audio ALBERT: A Lite BERT for Self-supervised Learning of Audio Representation
http://arxiv.org/abs/2005.08575
AUTHORS:        Po-Han Chi ; Pei-Hung Chung ; Tsung-Han Wu ; Chun-Cheng Hsieh ; Shang-Wen Li ; Hung-yi Lee
COMMENTS:       5 pages, 6 figures
HIGHLIGHT:      In this paper, we propose Audio ALBERT, a lite version of the self-supervised speech representation model.

107, TITLE:     Single-Stage Semantic Segmentation from Image Labels
http://arxiv.org/abs/2005.08104
AUTHORS:        Nikita Araslanov ; Stefan Roth
COMMENTS:       To appear at CVPR 2020; minor corrections in Eq. (9). Code: https://github.com/visinf/1-stage-wseg
HIGHLIGHT:      We show that despite its simplicity, our method achieves results that are competitive with significantly more complex pipelines, substantially outperforming earlier single-stage methods.

108, TITLE:     Learning Probabilistic Sentence Representations from Paraphrases
http://arxiv.org/abs/2005.08105
AUTHORS:        Mingda Chen ; Kevin Gimpel
COMMENTS:       Repl4NLP at ACL 2020, short paper
HIGHLIGHT:      In this paper we define probabilistic models that produce distributions for sentences.

109, TITLE:     Analytic Signal Phase in $N-D$ by Linear Symmetry Tensor--fingerprint modeling
http://arxiv.org/abs/2005.08108
AUTHORS:        Josef Bigun ; Fernando Alonso-Fernandez
HIGHLIGHT:      We reveal that the Analytic Signal phase, and its gradient have a hitherto unstudied discontinuity in $2-D $ and higher dimensions.

110, TITLE:     Efficient Wait-k Models for Simultaneous Machine Translation
http://arxiv.org/abs/2005.08595
AUTHORS:        Maha Elbayad ; Laurent Besacier ; Jakob Verbeek
HIGHLIGHT:      Wait-k decoders offer a simple but efficient approach for this problem.

111, TITLE:     RPD: A Distance Function Between Word Embeddings
http://arxiv.org/abs/2005.08113
AUTHORS:        Xuhui Zhou ; Zaixiang Zheng ; Shujian Huang
COMMENTS:       ACL Student Research Workshop 2020
HIGHLIGHT:      In this paper, we propose a novel metric called Relative pairwise inner Product Distance (RPD) to quantify the distance between different sets of word embeddings.

112, TITLE:     Mutual Information Maximization for Robust Plannable Representations
http://arxiv.org/abs/2005.08114
AUTHORS:        Yiming Ding ; Ignasi Clavera ; Pieter Abbeel
COMMENTS:       Accepted at NeurIPS 2019 Workshop on Robot Learning: Control and Interaction in the Real World
HIGHLIGHT:      In this work, we present MIRO, an information theoretic representational learning algorithm for model-based reinforcement learning.

113, TITLE:     From Boundaries to Bumps: when closed (extremal) contours are critical
http://arxiv.org/abs/2005.08116
AUTHORS:        Benjamin Kunsberg ; Steven W. Zucker
HIGHLIGHT:      From Boundaries to Bumps: when closed (extremal) contours are critical

114, TITLE:     That Sounds Familiar: an Analysis of Phonetic Representations Transfer Across Languages
http://arxiv.org/abs/2005.08118
AUTHORS:        Piotr Żelasko ; Laureano Moro-Velázquez ; Mark Hasegawa-Johnson ; Odette Scharenborg ; Najim Dehak
COMMENTS:       Submitted to Interspeech 2020. For some reason, the ArXiv Latex engine rendered it in more than 4 pages
HIGHLIGHT:      In this work, we focus on gaining a deeper understanding of how general these representations might be, and how individual phones are getting improved in a multilingual setting.

115, TITLE:     Neural Collaborative Reasoning
http://arxiv.org/abs/2005.08129
AUTHORS:        Hanxiong Chen ; Shaoyun Shi ; Yunqi Li ; Yongfeng Zhang
COMMENTS:       10 pages, 5 figures
HIGHLIGHT:      Inspired by recent progress on neural-symbolic machine learning, we propose a framework to integrate the power of embedding learning and logical reasoning, where the embeddings capture similarity patterns in data from perceptual perspectives, and the logic facilitates cognitive reasoning for informed decision making.

116, TITLE:     VPR-Bench: An Open-Source Visual Place Recognition Evaluation Framework with Quantifiable Viewpoint and Appearance Change
http://arxiv.org/abs/2005.08135
AUTHORS:        Mubariz Zaffar ; Shoaib Ehsan ; Michael Milford ; David Flynn ; Klaus McDonald-Maier
COMMENTS:       Currently under-review, 25 pages, 16 figures
HIGHLIGHT:      In this paper we address these key challenges through a new comprehensive open-source evaluation framework, dubbed 'VPR-Bench'.

117, TITLE:     Train in Germany, Test in The USA: Making 3D Object Detectors Generalize
http://arxiv.org/abs/2005.08139
AUTHORS:        Yan Wang ; Xiangyu Chen ; Yurong You ; Li Erran ; Bharath Hariharan ; Mark Campbell ; Kilian Q. Weinberger ; Wei-Lun Chao
COMMENTS:       Accepted to 2020 Conference on Computer Vision and Pattern Recognition (CVPR 2020)
HIGHLIGHT:      In this paper we consider the task of adapting 3D object detectors from one dataset to another.

118, TITLE:     IntelliCode Compose: Code Generation Using Transformer
http://arxiv.org/abs/2005.08025
AUTHORS:        Alexey Svyatkovskiy ; Shao Kun Deng ; Shengyu Fu ; Neel Sundaresan
COMMENTS:       15 pages, 6 figures
HIGHLIGHT:      In this paper, we introduce IntelliCode Compose $-$ a general-purpose multilingual code completion tool which is capable of predicting sequences of code tokens of arbitrary types, generating up to entire lines of syntactically correct code.

119, TITLE:     Semi-supervised Learning for Multi-speaker Text-to-speech Synthesis Using Discrete Speech Representation
http://arxiv.org/abs/2005.08024
AUTHORS:        Tao Tu ; Yuan-Jui Chen ; Alexander H. Liu ; Hung-yi Lee
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this work, we propose a semi-supervised learning approach for multi-speaker TTS.

120, TITLE:     Various Total Variation for Snapshot Video Compressive Imaging
http://arxiv.org/abs/2005.08028
AUTHORS:        Xin Yuan
COMMENTS:       5 pages, 4 figures
HIGHLIGHT:      This paper aims to answer the question of which TV penalty (anisotropic TV, isotropic TV and vectorized TV) works best for video SCI reconstruction?

121, TITLE:     Streaming Transformer-based Acoustic Models Using Self-attention with Augmented Memory
http://arxiv.org/abs/2005.08042
AUTHORS:        Chunyang Wu ; Yongqiang Wang ; Yangyang Shi ; Ching-Feng Yeh ; Frank Zhang
COMMENTS:       submitted to Interspeech 2020
HIGHLIGHT:      In this work, we proposed a novel augmentedmemory self-attention, which attends on a short segment of theinput sequence and a bank of memories.

122, TITLE:     Visual Relationship Detection using Scene Graphs: A Survey
http://arxiv.org/abs/2005.08045
AUTHORS:        Aniket Agarwal ; Ayush Mangal ;  Vipul
HIGHLIGHT:      In this paper, we present a detailed survey on the various techniques for scene graph generation, their efficacy to represent visual relationships and how it has been used to solve various downstream tasks.

123, TITLE:     Exploration of Audio Quality Assessment and Anomaly Localisation Using Attention Models
http://arxiv.org/abs/2005.08053
AUTHORS:        Qiang Huang ; Thomas Hain
COMMENTS:       Submitted to InterSpeech 2020
HIGHLIGHT:      In this paper, a novel model for audio quality assessment is proposed by jointly using bidirectional long short-term memory and an attention mechanism.

124, TITLE:     Recurrent Chunking Mechanisms for Long-Text Machine Reading Comprehensio
http://arxiv.org/abs/2005.08056
AUTHORS:        Hongyu Gong ; Yelong Shen ; Dian Yu ; Jianshu Chen ; Dong Yu
HIGHLIGHT:      In this paper, we study machine reading comprehension (MRC) on long texts, where a model takes as inputs a lengthy document and a question and then extracts a text span from the document as an answer.

125, TITLE:     Distributed Bounded Model Checking
http://arxiv.org/abs/2005.08063
AUTHORS:        Prantik Chatterjee ; Subhajit Roy ; Bui Phi Diep ; Akash Lal
HIGHLIGHT:      We present an algorithm that dynamically unfolds the call graph of the program and frequently splits it to create sub-tasks that can be solved in parallel.

126, TITLE:     Model-Augmented Actor-Critic: Backpropagating through Paths
http://arxiv.org/abs/2005.08068
AUTHORS:        Ignasi Clavera ; Violet Fu ; Pieter Abbeel
COMMENTS:       Accepted paper at ICLR 2020
HIGHLIGHT:      In this paper, we show how to make more effective use of the model by exploiting its differentiability.

127, TITLE:     Ontology and Cognitive Outcomes
http://arxiv.org/abs/2005.08078
AUTHORS:        David Limbaugh ; David Kasmier ; Ronald Rudnicki ; James Llinas ; Barry Smith
COMMENTS:       15 pages, 3 figures
HIGHLIGHT:      Herein we describe an approach to utilizing outcomes-based learning (OBL) to support these efforts that is based on an ontology of the cognitive processes performed by intelligence analysts.

128, TITLE:     A Robust Experimental Evaluation of Automated Multi-Label Classification Methods
http://arxiv.org/abs/2005.08083
AUTHORS:        Alex G. C. de Sá ; Cristiano G. Pimenta ; Gisele L. Pappa ; Alex A. Freitas
COMMENTS:       GECCO'2020 paper: Submitted and accepted
HIGHLIGHT:      In this work, we provide a general comparison of five automated multi-label classification methods -- two evolutionary methods, one Bayesian optimization method, one random search and one greedy search -- on 14 datasets and three designed search spaces.

129, TITLE:     Universal Adversarial Perturbations: A Survey
http://arxiv.org/abs/2005.08087
AUTHORS:        Ashutosh Chaubey ; Nikhil Agrawal ; Kavya Barnwal ; Keerat K. Guliani ; Pramod Mehta
COMMENTS:       20 pages, 17 figures
HIGHLIGHT:      In this paper, we attempt to provide a detailed discussion on the various data-driven and data-independent methods for generating universal perturbations, along with measures to defend against such perturbations.

130, TITLE:     Layer-Wise Cross-View Decoding for Sequence-to-Sequence Learning
http://arxiv.org/abs/2005.08081
AUTHORS:        Fenglin Liu ; Xuancheng Ren ; Guangxiang Zhao ; Xu Sun
COMMENTS:       Achieve state-of-the-art BLEU scores on WMT14 EN-DE, EN-FR, and IWSLT DE-EN datasets
HIGHLIGHT:      In this work, we explore to reuse the representations from different encoder layers for layer-wise cross-view decoding, that is, different views of the source sequences are presented to different decoder layers.

131, TITLE:     Improving Robustness using Joint Attention Network For Detecting Retinal Degeneration From Optical Coherence Tomography Images
http://arxiv.org/abs/2005.08094
AUTHORS:        Sharif Amit Kamran ; Alireza Tavakkoli ; Stewart Lee Zuckerbrod
COMMENTS:       \c{opyright} 20XX IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      In this paper we propose the use of disease-specific feature representation as a novel architecture comprised of two joint networks -- one for supervised encoding of disease model and the other for producing attention maps in an unsupervised manner to retain disease specific spatial information.

132, TITLE:     Reducibility and Statistical-Computational Gaps from Secret Leakage
http://arxiv.org/abs/2005.08099
AUTHORS:        Matthew Brennan ; Guy Bresler
COMMENTS:       175 pages; subsumes preliminary draft arXiv:1908.06130
HIGHLIGHT:      The insight in this work is that a slight generalization of the planted clique conjecture -- secret leakage planted clique -- gives rise to a variety of new average-case reduction techniques, yielding a web of reductions among problems with very different structure.

133, TITLE:     FiberStars: Visual Comparison of Diffusion Tractography Data between Multiple Subjects
http://arxiv.org/abs/2005.08090
AUTHORS:        Loraine Franke ; Daniel Karl I. Weidele ; Fan Zhang ; Suheyla Cetin-Karayumak ; Steve Pieper ; Lauren J. O'Donnell ; Yogesh Rathi ; Daniel Haehn
COMMENTS:       10 pages, 9 figures
HIGHLIGHT:      In this paper, we present the design and implementation of FiberStars, a visual analysis tool for tractography data that allows the interactive and scalable visualization of brain fiber clusters in 2D and 3D.

134, TITLE:     Imposing Regulation on Advanced Algorithms
http://arxiv.org/abs/2005.08092
AUTHORS:        Fotios Fitsilis
COMMENTS:       XXI, 82 pages, 5 figures. Cham: Springer
HIGHLIGHT:      Imposing Regulation on Advanced Algorithms

135, TITLE:     Approximation Algorithms and Hardness for Strong Unique Games
http://arxiv.org/abs/2005.08918
AUTHORS:        Suprovat Ghoshal ; Anand Louis
COMMENTS:       67 Pages
HIGHLIGHT:      In this paper, we give new algorithmic and hardness results for STRONG UNIQUE GAMES.

136, TITLE:     Joint Multi-Dimension Pruning
http://arxiv.org/abs/2005.08931
AUTHORS:        Zechun Liu ; Xiangyu Zhang ; Zhiqiang Shen ; Zhe Li ; Yichen Wei ; Kwang-Ting Cheng ; Jian Sun
HIGHLIGHT:      We present joint multi-dimension pruning (named as JointPruning), a new perspective of pruning a network on three crucial aspects: spatial, depth and channel simultaneously.

137, TITLE:     Reconstructing Maps from Text
http://arxiv.org/abs/2005.08932
AUTHORS:        Johnathan E. Avery ; Robert L. Goldstone ; Michael N. Jones
HIGHLIGHT:      In this paper we investigate the statistical sources required in language to infer maps, and resulting constraints placed on mechanisms of semantic representation.

138, TITLE:     Portrait Shadow Manipulation
http://arxiv.org/abs/2005.08925
AUTHORS:        Xuaner Cecilia Zhang ; J onathan T. Barron ; Yun-Ta Tsai ; Rohit Pandey ; Xiuming Zhang ; Ren Ng ; David E. Jacobs
COMMENTS:       SIGGRAPH 2020;Project webpage: https://people.eecs.berkeley.edu/~cecilia77/project-pages/portrait Video: https://youtu.be/M_qYTXhzyac
HIGHLIGHT:      In this paper, we present a computational approach that gives casual photographers some of this control, thereby allowing poorly-lit portraits to be relit post-capture in a realistic and easily-controllable way. To train our first network we construct a dataset of real-world portraits wherein synthetic foreign shadows are rendered onto the face, and we show that our network learns to remove those unwanted shadows.

139, TITLE:     Uncovering Gender Bias in Media Coverage of Politicians with Machine Learning
http://arxiv.org/abs/2005.07734
AUTHORS:        Susan Leavy
COMMENTS:       24 pages, 1 figures, 14 tables, Digital Scholarship in Humanities Journal
HIGHLIGHT:      This paper presents research uncovering systematic gender bias in the representation of political leaders in the media, using artificial intelligence.

140, TITLE:     Semantic Photo Manipulation with a Generative Image Prior
http://arxiv.org/abs/2005.07727
AUTHORS:        David Bau ; Hendrik Strobelt ; William Peebles ;  Jonas ; Bolei Zhou ; Jun-Yan Zhu ; Antonio Torralba
COMMENTS:       SIGGRAPH 2019
HIGHLIGHT:      In this paper, we address these issues by adapting the image prior learned by GANs to image statistics of an individual image.

141, TITLE:     Disentangling in Latent Space by Harnessing a Pretrained Generator
http://arxiv.org/abs/2005.07728
AUTHORS:        Yotam Nitzan ; Amit Bermano ; Yangyan Li ; Daniel Cohen-Or
COMMENTS:       17 pages, 10 figures
HIGHLIGHT:      In this paper, we present a method that learn show to represent data in a disentangled way, with minimal supervision, manifested solely using available pre-trained networks.

142, TITLE:     In Layman's Terms: Semi-Open Relation Extraction from Scientific Texts
http://arxiv.org/abs/2005.07751
AUTHORS:        Ruben Kruiper ; Julian F. V. Vincent ; Jessica Chen-Burger ; Marc P. Y. Desmulliez ; Ioannis Konstas
COMMENTS:       To be published in ACL 2020 conference proceedings
HIGHLIGHT:      In this work we combine the output of both types of systems to achieve Semi-Open Relation Extraction, a new task that we explore in the Biology domain.

143, TITLE:     A Scientific Information Extraction Dataset for Nature Inspired Engineering
http://arxiv.org/abs/2005.07753
AUTHORS:        Ruben Kruiper ; Julian F. V. Vincent ; Jessica Chen-Burger ; Marc P. Y. Desmulliez ; Ioannis Konstas
COMMENTS:       Published in Proceedings of the 12th Conference on Language Resources and Evaluation (LREC 2020)
HIGHLIGHT:      This paper describes a dataset of 1,500 manually-annotated sentences that express domain-independent relations between central concepts in a scientific biology text, such as trade-offs and correlations.

144, TITLE:     Design Choices for X-vector Based Speaker Anonymization
http://arxiv.org/abs/2005.08601
AUTHORS:        Brij Mohan Lal Srivastava ; Natalia Tomashenko ; Xin Wang ; Emmanuel Vincent ; Junichi Yamagishi ; Mohamed Maouche ; Aurélien Bellet ; Marc Tommasi
HIGHLIGHT:      In this paper, we present a flexible pseudo-speaker selection technique as a baseline for the first VoicePrivacy Challenge.

145, TITLE:     Brain-inspired Distributed Cognitive Architecture
http://arxiv.org/abs/2005.08603
AUTHORS:        Leendert A Remmelzwaal ; Amit K Mishra ; George F R Ellis
HIGHLIGHT:      In this paper we present a brain-inspired cognitive architecture that incorporates sensory processing, classification, contextual prediction, and emotional tagging.

146, TITLE:     The presence of occupational structure in online texts based on word embedding NLP models
http://arxiv.org/abs/2005.08612
AUTHORS:        Zoltán Kmetty ; Julia Koltai ; Tamás Rudas
COMMENTS:       34 pages, 2 figures, 4 tables. Paper presented at IC2S2 2019 and RC28 summer meeting 2019 (Columbia University)
HIGHLIGHT:      This research focuses on the positions of occupations in the semantic space represented by large amounts of textual data.

147, TITLE:     DDD20 End-to-End Event Camera Driving Dataset: Fusing Frames and Events with Deep Learning for Improved Steering Prediction
http://arxiv.org/abs/2005.08605
AUTHORS:        Yuhuang Hu ; Jonathan Binas ; Daniel Neil ; Shih-Chii Liu ; Tobi Delbruck
COMMENTS:       Accepted in The 23rd IEEE International Conference on Intelligent Transportation Systems (Special Session: Beyond Traditional Sensing for Intelligent Transportation)
HIGHLIGHT:      To enable studies of using event cameras in automobile driving applications, this paper reports a new end-to-end driving dataset called DDD20.

148, TITLE:     Decoder Modulation for Indoor Depth Completion
http://arxiv.org/abs/2005.08607
AUTHORS:        Dmitry Senushkin ; Ilia Belikov ; Anton Konushin
HIGHLIGHT:      The main contributions of our work are two-fold.

149, TITLE:     End-to-End Lip Synchronisation
http://arxiv.org/abs/2005.08606
AUTHORS:        You Jin Kim ; Hee Soo Heo ; Soo-Whan Chung ; Bong-Jin Lee
COMMENTS:       interspeech 2020 submit
HIGHLIGHT:      The goal of this work is to synchronise audio and video of a talking face using deep neural network models.

150, TITLE:     On the Hardness of Red-Blue Pebble Games
http://arxiv.org/abs/2005.08609
AUTHORS:        Pál András Papp ; Roger Wattenhofer
HIGHLIGHT:      We present various hardness results in different red-blue pebbling variants, with a focus on the oneshot model.

151, TITLE:     C3VQG: Category Consistent Cyclic Visual Question Generation
http://arxiv.org/abs/2005.07771
AUTHORS:        Shagun Uppal ; Anish Madan ; Sarthak Bhagat ; Yi Yu ; Rajiv Ratn Shah
HIGHLIGHT:      In this paper, we try to exploit the different visual cues and concepts in an image to generate questions using a variational autoencoder without the need for ground-truth answers.

152, TITLE:     Evolving Antennas for Ultra-High Energy Neutrino Detection
http://arxiv.org/abs/2005.07772
AUTHORS:        Julie Rolla ; Amy Connolly ; Kai Staats ; Stephanie Wissel ; Dean Arakaki ; Ian Best ; Adam Blenk ; Brian Clark ; Maximillian Clowdus ; Suren Gourapura ; Corey Harris ; Hannah Hasan ; Luke Letwin ; David Liu ; Carl Pfendner ; Jordan Potter ; Cade Sbrocco ; Tom Sinha ; Jacob Trevithick
COMMENTS:       8 pages including references, 6 figures, presented at 36th International Cosmic Ray Conference (ICRC 2019)
HIGHLIGHT:      Evolving Antennas for Ultra-High Energy Neutrino Detection

153, TITLE:     Learn Class Hierarchy using Convolutional Neural Networks
http://arxiv.org/abs/2005.08622
AUTHORS:        Riccardo La Grassa ; Ignazio Gallo ; Nicola Landro
COMMENTS:       7 pages
HIGHLIGHT:      In this paper, we propose a new architecture for hierarchical classification of images, introducing a stack of deep linear layers with cross-entropy loss functions and center loss combined.

154, TITLE:     Universalization of any adversarial attack using very few test examples
http://arxiv.org/abs/2005.08632
AUTHORS:        Sandesh Kamath ; Amit Deshpande ; K V Subrahmanyam
HIGHLIGHT:      In this paper, we propose a simple universalization technique to take any input-dependent adversarial attack and construct a universal attack by only looking at very few adversarial test examples.

155, TITLE:     A Learning-from-noise Dilated Wide Activation Network for denoising Arterial Spin Labeling (ASL) Perfusion Images
http://arxiv.org/abs/2005.07784
AUTHORS:        Danfeng Xie ; Yiran Li ; Hanlu Yang ; Li Bai ; Lei Zhang ; Ze Wang
HIGHLIGHT:      In this study, we proposed a new ASLDN to test whether similar or even better ASL CBF image quality can be achieved in the case of highly noisy training reference.

156, TITLE:     A flexible, extensible software framework for model compression based on the LC algorithm
http://arxiv.org/abs/2005.07786
AUTHORS:        Yerlan Idelbayev ; Miguel Á. Carreira-Perpiñán
COMMENTS:       15 pages, 4 figures, 2 tables
HIGHLIGHT:      We propose a software framework based on the ideas of the Learning-Compression (LC) algorithm, that allows a user to compress a neural network or other machine learning model using different compression schemes with minimal effort.

157, TITLE:     WW-Nets: Dual Neural Networks for Object Detection
http://arxiv.org/abs/2005.07787
AUTHORS:        Mohammad K. Ebrahimpour ; J. Ben Falandays ; Samuel Spevack ; Ming-Hsuan Yang ; David C. Noelle
COMMENTS:       8 pages, 3 figures
HIGHLIGHT:      We propose a new deep convolutional neural network framework that uses object location knowledge implicit in network connection weights to guide selective attention in object detection tasks.

158, 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:       22 pages, 6 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 ).

159, TITLE:     Transformation Based Deep Anomaly Detection in Astronomical Images
http://arxiv.org/abs/2005.07779
AUTHORS:        Esteban Reyes ; Pablo A. Estévez
COMMENTS:       8 pages, 6 figures, 4 tables. Accepted for publication in proceedings of the IEEE World Congress on Computational Intelligence (IEEE WCCI), Glasgow, UK, 19-24 July, 2020
HIGHLIGHT:      In this work, we propose several enhancements to a geometric transformation based model for anomaly detection in images (GeoTranform).

160, TITLE:     FuSSI-Net: Fusion of Spatio-temporal Skeletons for Intention Prediction Network
http://arxiv.org/abs/2005.07796
AUTHORS:        Francesco Piccoli ; Rajarathnam Balakrishnan ; Maria Jesus Perez ; Moraldeepsingh Sachdeo ; Carlos Nunez ; Matthew Tang ; Kajsa Andreasson ; Kalle Bjurek ; Ria Dass Raj ; Ebba Davidsson ; Colin Eriksson ; Victor Hagman ; Jonas Sjoberg ; Ying Li ; L. Srikar Muppirisetty ; Sohini Roychowdhury
COMMENTS:       5 pages, 6 figures, 5 tables, IEEE Asilomar SSC
HIGHLIGHT:      In this work, we develop an end-to-end pedestrian intention framework that performs well on day- and night- time scenarios.

161, TITLE:     JDI-T: Jointly trained Duration Informed Transformer for Text-To-Speech without Explicit Alignment
http://arxiv.org/abs/2005.07799
AUTHORS:        Dan Lim ; Won Jang ; Gyeonghwan O ; Hyeyeong Park ; Bongwan Kim ; Jesam Yoon
COMMENTS:       submitted to INTERSPEECH 2020
HIGHLIGHT:      We propose Jointly trained Duration Informed Transformer (JDI-T), a feed-forward Transformer with a duration predictor jointly trained without explicit alignments in order to generate an acoustic feature sequence from an input text.

162, TITLE:     Building BROOK: A Multi-modal and Facial Video Database for Human-Vehicle Interaction Research
http://arxiv.org/abs/2005.08637
AUTHORS:        Xiangjun Peng ; Zhentao Huang ; Xu Sun
COMMENTS:       Conference: ACM CHI Conference on Human Factors in Computing Systems Workshops (CHI'20 Workshops)At: Honolulu, Hawaii, USA URL:https://emergentdatatrails.com
HIGHLIGHT:      In this paper, we present our work-in-progress BROOK, a public multi-modal database with facial video records, which could be used to characterize drivers' affective states and driving styles.

163, TITLE:     Conversational Search -- A Report from Dagstuhl Seminar 19461
http://arxiv.org/abs/2005.08658
AUTHORS:        Avishek Anand ; Lawrence Cavedon ; Matthias Hagen ; Hideo Joho ; Mark Sanderson ; Benno Stein
COMMENTS:       contains arXiv:2001.06910, arXiv:2001.02912
HIGHLIGHT:      The ideas and findings presented in this report should serve as one of the main sources for diverse research programs on Conversational Search.

164, TITLE:     An Overview of Privacy in Machine Learning
http://arxiv.org/abs/2005.08679
AUTHORS:        Emiliano De Cristofaro
HIGHLIGHT:      In this document, we set to review privacy challenges in this space, providing a systematic review of the relevant research literature, also exploring possible countermeasures.

165, TITLE:     Building a Hebrew Semantic Role Labeling Lexical Resource from Parallel Movie Subtitles
http://arxiv.org/abs/2005.08206
AUTHORS:        Ben Eyal ; Michael Elhadad
COMMENTS:       9 pages, 7 figures, accepted to LREC 2020
HIGHLIGHT:      We present a semantic role labeling resource for Hebrew built semi-automatically through annotation projection from English.

166, TITLE:     Quantifying the Impact on Software Complexity of Composable Inductive Programming using Zoea
http://arxiv.org/abs/2005.08211
AUTHORS:        Edward McDaid ; Sarah McDaid
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      This paper presents the results of a quantitative comparison of the software complexity of equivalent code implemented in Zoea and also in a conventional programming language.

167, TITLE:     Speech to Text Adaptation: Towards an Efficient Cross-Modal Distillation
http://arxiv.org/abs/2005.08213
AUTHORS:        Won Ik Cho ; Donghyun Kwak ; Jiwon Yoon ; Nam Soo Kim
COMMENTS:       Preprint; 5 pages, 1 figure, 4 tables
HIGHLIGHT:      We demonstrate the validity of our proposal upon the performance on the Fluent Speech Command dataset.

168, TITLE:     Learning Individual Speaking Styles for Accurate Lip to Speech Synthesis
http://arxiv.org/abs/2005.08209
AUTHORS:        K R Prajwal ; Rudrabha Mukhopadhyay ; Vinay Namboodiri ; C V Jawahar
COMMENTS:       10 pages (including references), 5 figures, Accepted in CVPR, 2020
HIGHLIGHT:      In this work, we explore the task of lip to speech synthesis, i.e., learning to generate natural speech given only the lip movements of a speaker. To this end, we collect and release a large-scale benchmark dataset, the first of its kind, specifically to train and evaluate the single-speaker lip to speech task in natural settings.

169, TITLE:     LiSSS: A toy corpus of Literary Spanish Sentences Sentiment for Emotions Detection
http://arxiv.org/abs/2005.08223
AUTHORS:        Juan-Manuel Torres-Moreno ; Luis-Gil Moreno-Jiménez
COMMENTS:       8 pages, 3 tables
HIGHLIGHT:      In this work we present a new and small corpus in the area of Computational Creativity (CC), the Literary Sentiment Sentence Spanish Corpus (LISSS).

170, TITLE:     Deep Learning for Community Detection: Progress, Challenges and Opportunities
http://arxiv.org/abs/2005.08225
AUTHORS:        Fanzhen Liu ; Shan Xue ; Jia Wu ; Chuan Zhou ; Wenbin Hu ; Cecile Paris ; Surya Nepal ; Jian Yang ; Philip S. Yu
COMMENTS:       Accepted Paper in the 29th International Joint Conference on Artificial Intelligence (IJCAI 20), Survey Track
HIGHLIGHT:      Structured into three broad research streams in this domain - deep neural networks, deep graph embedding, and graph neural networks, this article summarizes the contributions of the various frameworks, models, and algorithms in each stream along with the current challenges that remain unsolved and the future research opportunities yet to be explored.

171, TITLE:     #Coronavirus or #Chinesevirus?!: Understanding the negative sentiment reflected in Tweets with racist hashtags across the development of COVID-19
http://arxiv.org/abs/2005.08224
AUTHORS:        Xin Pei ; Deval Mehta
HIGHLIGHT:      Especially, we propose a stage-based approach to capture how the negative sentiment changes along with the three development stages of COVID-19, under which it transformed from a domestic epidemic into an international public health emergency and later, into the global pandemic.

172, TITLE:     Graph Density-Aware Losses for Novel Compositions in Scene Graph Generation
http://arxiv.org/abs/2005.08230
AUTHORS:        Boris Knyazev ; Harm de Vries ; Cătălina Cangea ; Graham W. Taylor ; Aaron Courville ; Eugene Belilovsky
COMMENTS:       17 pages, the code is available at https://github.com/bknyaz/sgg
HIGHLIGHT:      In this paper, we identify two key issues that limit such generalization.

173, TITLE:     Studying the Transfer of Biases from Programmers to Programs
http://arxiv.org/abs/2005.08231
AUTHORS:        Tore Pedersen ; Christian Johansen ; Johanna Johansen
COMMENTS:       40 pages of which 7 pages of Appendix, 26 Figures, 2 Tables
HIGHLIGHT:      It is generally agreed that one origin of machine bias is resulting from characteristics within the dataset on which the algorithms are trained, i.e., the data does not warrant a generalized inference.

174, TITLE:     FuCiTNet: Improving the generalization of deep learning networks by the fusion of learned class-inherent transformations
http://arxiv.org/abs/2005.08235
AUTHORS:        Manuel Rey-Area ; Emilio Guirado ; Siham Tabik ; Javier Ruiz-Hidalgo
HIGHLIGHT:      This work presents a new approach, independent but complementary to the previous mentioned techniques, for improving the generalization of DNNs on very small datasets in which the involved classes share many visual features.

175, TITLE:     Dual Learning: Theoretical Study and an Algorithmic Extension
http://arxiv.org/abs/2005.08238
AUTHORS:        Zhibing Zhao ; Yingce Xia ; Tao Qin ; Lirong Xia ; Tie-Yan Liu
COMMENTS:       11 pages, 2 figures
HIGHLIGHT:      In this paper, we aim at understanding why and when dual learning works.

176, TITLE:     Dampen the Stop-and-Go Traffic with Connected and Automated Vehicles -- A Deep Reinforcement Learning Approach
http://arxiv.org/abs/2005.08245
AUTHORS:        Liming Jiang ; Yuanchang Xie ; Danjue Chen ; Tienan Li ; Nicholas G. Evans
HIGHLIGHT:      Instead of using analytical model, this study adopts reinforcement learning to control the behavior of CAV and put a single CAV at the 2nd position of a vehicle fleet with the purpose to dampen the speed oscillation from the fleet leader and help following human drivers adopt more smooth driving behavior.

177, TITLE:     On the Combined Use of Extrinsic Semantic Resources for Medical Information Search
http://arxiv.org/abs/2005.08259
AUTHORS:        Mohammed Maree ; Israa Noor ; Khaled Rabayah ; Mohammed Belkhatir ; Saadat M. Alhashmi
HIGHLIGHT:      In this article, we explore the combination of multiple extrinsic semantic resources in the development of a full-fledged medical information search framework to: i) highlight and expand head medical concepts in verbose medical queries (i.e. concepts among query terms that significantly contribute to the informativeness and intent of a given query), ii) build semantically enhanced inverted index documents, iii) contribute to a heuristical weighting technique in the query document matching process.

178, TITLE:     High-dimensional Convolutional Networks for Geometric Pattern Recognition
http://arxiv.org/abs/2005.08144
AUTHORS:        Christopher Choy ; Junha Lee ; Rene Ranftl ; Jaesik Park ; Vladlen Koltun
COMMENTS:       Accepted for CVPR 2020 oral presentation
HIGHLIGHT:      We present high-dimensional convolutional networks (ConvNets) for pattern recognition problems that arise in the context of geometric registration.

179, TITLE:     Semi-Automating Knowledge Base Construction for Cancer Genetics
http://arxiv.org/abs/2005.08146
AUTHORS:        Somin Wadhwa ; Kanhua Yin ; Kevin S. Hughes ; Byron C. Wallace
COMMENTS:       In proceedings of Automated Knowledge Base Construction (AKBC), 2020
HIGHLIGHT:      In this work, we consider the exponentially growing subarea of genetics in cancer.

180, TITLE:     Adversarial Training for Commonsense Inference
http://arxiv.org/abs/2005.08156
AUTHORS:        Lis Pereira ; Xiaodong Liu ; Fei Cheng ; Masayuki Asahara ; Ichiro Kobayashi
COMMENTS:       6 pages, Accepted to ACL2020 RepL4NLP workshop
HIGHLIGHT:      We propose an AdversariaL training algorithm for commonsense InferenCE (ALICE).

181, 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.

182, TITLE:     FA-GANs: Facial Attractiveness Enhancement with Generative Adversarial Networks on Frontal Faces
http://arxiv.org/abs/2005.08168
AUTHORS:        Jingwu He ; Chuan Wang ; Yang Zhang ; Jie Guo ; Yanwen Guo
HIGHLIGHT:      In this paper, we propose the first Generative Adversarial Networks (GANs) for enhancing facial attractiveness in both geometry and appearance aspects, which we call "FA-GANs".

183, TITLE:     Neural Networks for Fashion Image Classification and Visual Search
http://arxiv.org/abs/2005.08170
AUTHORS:        Fengzi Li ; Shashi Kant ; Shunichi Araki ; Sumer Bangera ; Swapna Samir Shukla
HIGHLIGHT:      In this paper, we explore machine learning algorithms which can help us solve both these problems.

184, TITLE:     Encodings of Source Syntax: Similarities in NMT Representations Across Target Languages
http://arxiv.org/abs/2005.08177
AUTHORS:        Tyler A. Chang ; Anna N. Rafferty
COMMENTS:       To appear at the 5th Workshop on Representation Learning for NLP
HIGHLIGHT:      We train neural machine translation (NMT) models from English to six target languages, using NMT encoder representations to predict ancestor constituent labels of source language words.

185, TITLE:     IMoJIE: Iterative Memory-Based Joint Open Information Extraction
http://arxiv.org/abs/2005.08178
AUTHORS:        Keshav Kolluru ; Samarth Aggarwal ; Vipul Rathore ;  Mausam ; Soumen Chakrabarti
HIGHLIGHT:      We present IMoJIE, an extension to CopyAttention, which produces the next extraction conditioned on all previously extracted tuples.

186, TITLE:     Multi-modal Automated Speech Scoring using Attention Fusion
http://arxiv.org/abs/2005.08182
AUTHORS:        Manraj Singh Grover ; Yaman Kumar ; Sumit Sarin ; Payman Vafaee ; Mika Hama ; Rajiv Ratn Shah
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this study, we propose a novel multi-modal end-to-end neural approach for automated assessment of non-native English speakers' spontaneous speech using attention fusion.

187, TITLE:     Co-occurrence Based Texture Synthesis
http://arxiv.org/abs/2005.08186
AUTHORS:        Anna Darzi ; Itai Lang ; Ashutosh Taklikar ; Hadar Averbuch-Elor ; Shai Avidan
HIGHLIGHT:      We model local texture patterns using the co-occurrence statistics of pixel values.

188, TITLE:     Cross-Lingual Low-Resource Set-to-Description Retrieval for Global E-Commerce
http://arxiv.org/abs/2005.08188
AUTHORS:        Juntao Li ; Chang Liu ; Jian Wang ; Lidong Bing ; Hongsong Li ; Xiaozhong Liu ; Dongyan Zhao ; Rui Yan
COMMENTS:       AAAI 2020
HIGHLIGHT:      In this paper, we explore a new task of cross-lingual information retrieval, i.e., cross-lingual set-to-description retrieval in cross-border e-commerce, which involves matching product attribute sets in the source language with persuasive product descriptions in the target language. We manually collect a new and high-quality paired dataset, where each pair contains an unordered product attribute set in the source language and an informative product description in the target language.

189, 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.

190, TITLE:     Hyperspectral Image Classification Based on Sparse Modeling of Spectral Blocks
http://arxiv.org/abs/2005.08191
AUTHORS:        Saeideh Ghanbari Azar ; Saeed Meshgini ; Tohid Yousefi Rezaii ; Soosan Beheshti
HIGHLIGHT:      In this paper, a sparse modeling framework is proposed for hyperspectral image classification.



==========Updates to Previous Papers==========
1, TITLE:       Resolution Adaptive Networks for Efficient Inference
http://arxiv.org/abs/2003.07326
AUTHORS:        Le Yang ; Yizeng Han ; Xi Chen ; Shiji Song ; Jifeng Dai ; Gao Huang
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we focus on spatial redundancy of input samples and propose a novel Resolution Adaptive Network (RANet), which is inspired by the intuition that low-resolution representations are sufficient for classifying "easy" inputs containing large objects with prototypical features, while only some "hard" samples need spatially detailed information.

2, TITLE:       Reconstructing Natural Scenes from fMRI Patterns using BigBiGAN
http://arxiv.org/abs/2001.11761
AUTHORS:        Milad Mozafari ; Leila Reddy ; Rufin VanRullen
HIGHLIGHT:      Here, we employ a recently proposed large-scale bi-directional generative adversarial network, called BigBiGAN, to decode and reconstruct natural scenes from fMRI patterns.

3, TITLE:       On-Policy Robot Imitation Learning from a Converging Supervisor
http://arxiv.org/abs/1907.03423
AUTHORS:        Ashwin Balakrishna ; Brijen Thananjeyan ; Jonathan Lee ; Felix Li ; Arsh Zahed ; Joseph E. Gonzalez ; Ken Goldberg
COMMENTS:       Conference on Robot Learning (CoRL) 2019 Oral. First two authors contributed equally
HIGHLIGHT:      Existing on-policy imitation learning algorithms, such as DAgger, assume access to a fixed supervisor.

4, TITLE:       FReeNet: Multi-Identity Face Reenactment
http://arxiv.org/abs/1905.11805
AUTHORS:        Jiangning Zhang ; Xianfang Zeng ; Mengmeng Wang ; Yusu Pan ; Liang Liu ; Yong Liu ; Yu Ding ; Changjie Fan
COMMENTS:       Add more experiments; Revise the paper carefully;
HIGHLIGHT:      This paper presents a novel multi-identity face reenactment framework, named FReeNet, to transfer facial expressions from an arbitrary source face to a target face with a shared model.

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

6, TITLE:       Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net
http://arxiv.org/abs/2003.11851
AUTHORS:        Lu Wang ; Dong-xue Liang ; Xiao-lei Yin ; Jing Qiu ; Zhi-yun Yang ; Jun-hui Xing ; Jian-zeng Dong ; Zhao-yuan Ma
HIGHLIGHT:      This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels.

7, TITLE:       A Dataset for Statutory Reasoning in Tax Law Entailment and Question Answering
http://arxiv.org/abs/2005.05257
AUTHORS:        Nils Holzenberger ; Andrew Blair-Stanek ; Benjamin Van Durme
HIGHLIGHT:      To investigate the performance of natural language understanding approaches on statutory reasoning, we introduce a dataset, together with a legal-domain text corpus.

8, TITLE:       A Continuous Information Gain Measure to Find the Most Discriminatory Problems for AI Benchmarking
http://arxiv.org/abs/1809.02904
AUTHORS:        Matthew Stephenson ; Damien Anderson ; Ahmed Khalifa ; John Levine ; Jochen Renz ; Julian Togelius ; Christoph Salge
COMMENTS:       8 pages, 1 figure, 2 tables
HIGHLIGHT:      This paper introduces an information-theoretic method for selecting a subset of problems which gives the most information about a group of problem-solving algorithms.

9, TITLE:       RISE Video Dataset: Recognizing Industrial Smoke Emissions
http://arxiv.org/abs/2005.06111
AUTHORS:        Yen-Chia Hsu ; Ting-Hao 'Kenneth' Huang ; Ting-Yao Hu ; Paul Dille ; Sean Prendi ; Ryan Hoffman ; Anastasia Tsuhlares ; Randy Sargent ; Illah Nourbakhsh
COMMENTS:       Technical report
HIGHLIGHT:      We introduce RISE, the first large-scale video dataset for Recognizing Industrial Smoke Emissions.

10, TITLE:      Cross-lingual Transfer of Twitter Sentiment Models Using a Common Vector Space
http://arxiv.org/abs/2005.07456
AUTHORS:        Marko Robnik-Sikonja ; Kristjan Reba ; Igor Mozetic
HIGHLIGHT:      We use cross-lingual word embeddings to transfer machine learning prediction models for Twitter sentiment between 13 languages.

11, TITLE:      Normalized Convolutional Neural Network
http://arxiv.org/abs/2005.05274
AUTHORS:        Dongsuk Kim ; Geonhee Lee ; Myungjae Lee ; Shin Uk Kang ; Dongmin Kim
COMMENTS:       6pages typo errors ,errata are fixed.(p1,2,4,5)
HIGHLIGHT:      In this paper, we propose Normalized Convolutional Neural Network(NCNN).

12, TITLE:      Survey on Visual Sentiment Analysis
http://arxiv.org/abs/2004.11639
AUTHORS:        Alessandro Ortis ; Giovanni Maria Farinella ; Sebastiano Battiato
COMMENTS:       This paper is a postprint of a paper accepted by IET Image Processing and is subject to Institution of Engineering and Technology Copyright. When the final version is published, the copy of record will be available at the IET Digital Library
HIGHLIGHT:      Visual Sentiment Analysis aims to understand how images affect people, in terms of evoked emotions.

13, TITLE:      Simulation Pipeline for Traffic Evacuation in Urban Areas and Emergency Traffic Management Policy Improvements
http://arxiv.org/abs/2002.06198
AUTHORS:        Yu Chen ; S. Yusef Shafi ; Yi-fan Chen
COMMENTS:       37 pages, 9 figures
HIGHLIGHT:      In this paper, we build a traffic simulation pipeline to explore the above problems, covering many aspects of evacuation, including map creation, demand generation, vehicle behavior, bottleneck identification, traffic management policy improvement, and results analysis.

14, TITLE:      Benchmarking End-to-End Behavioural Cloning on Video Games
http://arxiv.org/abs/2004.00981
AUTHORS:        Anssi Kanervisto ; Joonas Pussinen ; Ville Hautamäki
COMMENTS:       To appear in IEEE Conference on Games 2020. Experiment code available at https://github.com/joonaspu/video-game-behavioural-cloning and https://github.com/joonaspu/ViControl
HIGHLIGHT:      We take a step towards a general approach and study the general applicability of behavioural cloning on twelve video games, including six modern video games (published after 2010), by using human demonstrations as training data.

15, TITLE:      Prosody Transfer in Neural Text to Speech Using Global Pitch and Loudness Features
http://arxiv.org/abs/1911.09645
AUTHORS:        Siddharth Gururani ; Kilol Gupta ; Dhaval Shah ; Zahra Shakeri ; Jervis Pinto
COMMENTS:       5 pages, in review for conference publication
HIGHLIGHT:      This paper presents a simple yet effective method to achieve prosody transfer from a reference speech signal to synthesized speech.

16, TITLE:      Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
http://arxiv.org/abs/2003.00330
AUTHORS:        Luis C. Lamb ; Artur Garcez ; Marco Gori ; Marcelo Prates ; Pedro Avelar ; Moshe Vardi
COMMENTS:       Updated version
HIGHLIGHT:      In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing.

17, TITLE:      ClovaCall: Korean Goal-Oriented Dialog Speech Corpus for Automatic Speech Recognition of Contact Centers
http://arxiv.org/abs/2004.09367
AUTHORS:        Jung-Woo Ha ; Kihyun Nam ; Jingu Kang ; Sang-Woo Lee ; Sohee Yang ; Hyunhoon Jung ; Eunmi Kim ; Hyeji Kim ; Soojin Kim ; Hyun Ah Kim ; Kyoungtae Doh ; Chan Kyu Lee ; Nako Sung ; Sunghun Kim
COMMENTS:       5 pages, 2 figures, 4 tables, The first two authors equally contributed to this work
HIGHLIGHT:      Here we introduce a new large-scale Korean call-based speech corpus under a goal-oriented dialog scenario from more than 11,000 people, i.e., ClovaCall corpus.

18, TITLE:      Towards Generalizable Surgical Activity Recognition Using Spatial Temporal Graph Convolutional Networks
http://arxiv.org/abs/2001.03728
AUTHORS:        Duygu Sarikaya ; Pierre Jannin
HIGHLIGHT:      We introduce a modality that is robust to scene variation, based on spatial temporal graph representations of surgical tools in videos, for surgical activity recognition.

19, TITLE:      HighwayGraph: Modelling Long-distance Node Relations for Improving General Graph Neural Network
http://arxiv.org/abs/1911.03904
AUTHORS:        Deli Chen ; Xiaoqian Liu ; Yankai Lin ; Peng Li ; Jie Zhou ; Qi Su ; Xu Sun
COMMENTS:       8 pages
HIGHLIGHT:      To address this issue, we propose to model long-distance node relations by simply relying on shallow GNN architectures with two solutions: (1) Implicitly modelling by learning to predict node pair relations (2) Explicitly modelling by adding edges between nodes that potentially have the same label.

20, TITLE:      Time-Delay Feedback Neural Network for Fast-Moving Small Target Discrimination Against Complex Dynamic Environments
http://arxiv.org/abs/2001.05846
AUTHORS:        Hongxin Wang ; Huatian Wang ; Jiannan Zhao ; Cheng Hu ; Jigen Peng ; Shigang Yue
COMMENTS:       13 pages, 16 figures
HIGHLIGHT:      In this paper, we propose a STMD-based neural network with feedback connection (Feedback STMD), where the network output is temporally delayed, then fed back to lower layers to mediate neural responses.

21, TITLE:      Multi-Task Network for Noise-Robust Keyword Spotting and Speaker Verification using CTC-based Soft VAD and Global Query Attention
http://arxiv.org/abs/2005.03867
AUTHORS:        Myunghun Jung ; Youngmoon Jung ; Jahyun Goo ; Hoirin Kim
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we propose a multi-task network that performs KWS and SV simultaneously to fully utilize the interrelated domain information.

22, TITLE:      CTC-synchronous Training for Monotonic Attention Model
http://arxiv.org/abs/2005.04712
AUTHORS:        Hirofumi Inaguma ; Masato Mimura ; Tatsuya Kawahara
HIGHLIGHT:      To address this problem, we propose CTC-synchronous training (CTC-ST), in which MoChA uses CTC alignments to learn optimal monotonic alignments.

23, TITLE:      Point Cloud Completion by Skip-attention Network with Hierarchical Folding
http://arxiv.org/abs/2005.03871
AUTHORS:        Xin Wen ; Tianyang Li ; Zhizhong Han ; Yu-Shen Liu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      To address this problem, we propose Skip-Attention Network (SA-Net) for 3D point cloud completion.

24, TITLE:      Towards High-Fidelity 3D Face Reconstruction from In-the-Wild Images Using Graph Convolutional Networks
http://arxiv.org/abs/2003.05653
AUTHORS:        Jiangke Lin ; Yi Yuan ; Tianjia Shao ; Kun Zhou
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we introduce a method to reconstruct 3D facial shapes with high-fidelity textures from single-view images in-the-wild, without the need to capture a large-scale face texture database.

25, TITLE:      Learning Hierarchical Teaching Policies for Cooperative Agents
http://arxiv.org/abs/1903.03216
AUTHORS:        Dong-Ki Kim ; Miao Liu ; Shayegan Omidshafiei ; Sebastian Lopez-Cot ; Matthew Riemer ; Golnaz Habibi ; Gerald Tesauro ; Sami Mourad ; Murray Campbell ; Jonathan P. How
COMMENTS:       Presented at AAMAS 2020; arXiv version added with the appendix
HIGHLIGHT:      This paper introduces a novel learning-to-teach framework, called hierarchical multiagent teaching (HMAT), that improves scalability to complex environments by using the deep representation for student policies and by advising with more expressive extended action sequences over multiple levels of temporal abstraction.

26, TITLE:      NIT-Agartala-NLP-Team at SemEval-2020 Task 8: Building Multimodal Classifiers to tackle Internet Humor
http://arxiv.org/abs/2005.06943
AUTHORS:        Steve Durairaj Swamy ; Shubham Laddha ; Basil Abdussalam ; Debayan Datta ; Anupam Jamatia
COMMENTS:       Submitted to International Workshop on Semantic Evaluation (SemEval)-2020 Task 8: Memotion Analysis, http://alt.qcri.org/semeval2020/index.php?id=tasks
HIGHLIGHT:      The paper describes the systems submitted to SemEval-2020 Task 8: Memotion by the `NIT-Agartala-NLP-Team'.

27, TITLE:      How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence
http://arxiv.org/abs/2004.12158
AUTHORS:        Haoxi Zhong ; Chaojun Xiao ; Cunchao Tu ; Tianyang Zhang ; Zhiyuan Liu ; Maosong Sun
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      In this paper, we introduce the history, the current state, and the future directions of research in LegalAI.

28, TITLE:      Rectification with Visual Sphere perspective: an algebraic alternative for P4P pose estimation
http://arxiv.org/abs/2004.08933
AUTHORS:        Jakub Maksymilian Fober
COMMENTS:       17 pages, 7 figures
HIGHLIGHT:      Presented algorithm solves P4P problem for tangent pair of coplanar parallel lines viewed in perspective with an algebraic equation.

29, TITLE:      Touchdown: Natural Language Navigation and Spatial Reasoning in Visual Street Environments
http://arxiv.org/abs/1811.12354
AUTHORS:        Howard Chen ; Alane Suhr ; Dipendra Misra ; Noah Snavely ; Yoav Artzi
COMMENTS:       arXiv admin note: text overlap with arXiv:1809.00786
HIGHLIGHT:      We study the problem of jointly reasoning about language and vision through a navigation and spatial reasoning task. We introduce the Touchdown task and dataset, where an agent must first follow navigation instructions in a real-life visual urban environment, and then identify a location described in natural language to find a hidden object at the goal position.

30, TITLE:      Generating Text through Adversarial Training using Skip-Thought Vectors
http://arxiv.org/abs/1808.08703
AUTHORS:        Afroz Ahamad
COMMENTS:       NAACL 2019: https://www.aclweb.org/anthology/N19-3008
HIGHLIGHT:      This study presents an approach to text generation using Skip-Thought sentence embeddings with GANs based on gradient penalty functions and f-measures.

31, TITLE:      A Convolutional Neural Network-based Patent Image Retrieval Method for Design Ideation
http://arxiv.org/abs/2003.08741
AUTHORS:        Shuo Jiang ; Jianxi Luo ; Guillermo Ruiz Pava ; Jie Hu ; Christopher L. Magee
COMMENTS:       11 pages, 11 figures
HIGHLIGHT:      Herein, we propose a convolutional neural network (CNN)-based patent image retrieval method.

32, TITLE:      Quantum query complexity of symmetric oracle problems
http://arxiv.org/abs/1812.09428
AUTHORS:        Daniel Copeland ; James Pommersheim
COMMENTS:       v2 25 pages, fixed proof of Prop. 5.6, added Section 7
HIGHLIGHT:      We study the query complexity of quantum learning problems in which the oracles form a group $G$ of unitary matrices.

33, TITLE:      Weakly Supervised Semantic Segmentation in 3D Graph-Structured Point Clouds of Wild Scenes
http://arxiv.org/abs/2004.12498
AUTHORS:        Haiyan Wang ; Xuejian Rong ; Liang Yang ; Jinglun Feng ; Jizhong Xiao ; Yingli Tian
COMMENTS:       13 pages, 8 figures, Under review as a journal paper at CVIU
HIGHLIGHT:      To alleviate this issue, we propose a novel deep graph convolutional network-based framework for large-scale semantic scene segmentation in point clouds with sole 2D supervision.

34, TITLE:      Deep speech inpainting of time-frequency masks
http://arxiv.org/abs/1910.09058
AUTHORS:        Mikolaj Kegler ; Pierre Beckmann ; Milos Cernak
COMMENTS:       Submitted to InterSpeech2020
HIGHLIGHT:      To address these limitations, here we propose an end-to-end framework for speech inpainting, the context-based retrieval of missing or severely distorted parts of time-frequency representation of speech.

35, TITLE:      ContextNet: Improving Convolutional Neural Networks for Automatic Speech Recognition with Global Context
http://arxiv.org/abs/2005.03191
AUTHORS:        Wei Han ; Zhengdong Zhang ; Yu Zhang ; Jiahui Yu ; Chung-Cheng Chiu ; James Qin ; Anmol Gulati ; Ruoming Pang ; Yonghui Wu
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we study how to bridge this gap and go beyond with a novel CNN-RNN-transducer architecture, which we call ContextNet.

36, TITLE:      A Critic Evaluation of Methods for COVID-19 Automatic Detection from X-Ray Images
http://arxiv.org/abs/2004.12823
AUTHORS:        Gianluca Maguolo ; Loris Nanni
HIGHLIGHT:      In this paper, we compare and evaluate different testing protocols used for automatic COVID-19 diagnosis from X-Ray images in the recent literature.

37, TITLE:      Digital Social Contracts: A Foundation for an Egalitarian and Just Digital Society
http://arxiv.org/abs/2005.06261
AUTHORS:        Luca Cardelli ; Gal Shahaf ; Ehud Shapiro ; Nimrod Talmon
HIGHLIGHT:      Here, we present a formal definition of a digital social contract as agents that communicate asynchronously via crypto-speech acts, where the output of each agent is the input of all the other agents.

38, TITLE:      Distilling neural networks into skipgram-level decision lists
http://arxiv.org/abs/2005.07111
AUTHORS:        Madhumita Sushil ; Simon Šuster ; Walter Daelemans
HIGHLIGHT:      To overcome these limitations, we propose a pipeline to explain RNNs by means of decision lists (also called rules) over skipgrams. For evaluation of explanations, we create a synthetic sepsis-identification dataset, as well as apply our technique on additional clinical and sentiment analysis datasets.

39, TITLE:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks
http://arxiv.org/abs/2004.06165
AUTHORS:        Xiujun Li ; Xi Yin ; Chunyuan Li ; Pengchuan Zhang ; Xiaowei Hu ; Lei Zhang ; Lijuan Wang ; Houdong Hu ; Li Dong ; Furu Wei ; Yejin Choi ; Jianfeng Gao
COMMENTS:       Code and pre-trained models are released: https://github.com/microsoft/Oscar
HIGHLIGHT:      Oscar: Object-Semantics Aligned Pre-training for Vision-Language Tasks

40, TITLE:      A Divide-and-Conquer Approach to the Summarization of Long Documents
http://arxiv.org/abs/2004.06190
AUTHORS:        Alexios Gidiotis ; Grigorios Tsoumakas
HIGHLIGHT:      We present a novel divide-and-conquer method for the neural summarization of long documents.

41, TITLE:      META-Learning State-based Eligibility Traces for More Sample-Efficient Policy Evaluation
http://arxiv.org/abs/1904.11439
AUTHORS:        Mingde Zhao ; Sitao Luan ; Ian Porada ; Xiao-Wen Chang ; Doina Precup
COMMENTS:       Accepted by AAMAS 2020
HIGHLIGHT:      For better sample efficiency of TD-learning, we propose a meta-learning method for adjusting the eligibility trace parameter, in a state-dependent manner.

42, TITLE:      KernelNet: A Data-Dependent Kernel Parameterization for Deep Generative Modeling
http://arxiv.org/abs/1912.00979
AUTHORS:        Yufan Zhou ; Changyou Chen ; Jinhui Xu
HIGHLIGHT:      To mitigate this burden, we propose in this paper a framework to construct and learn a data-dependent kernel based on random features and implicit spectral distributions parameterized by deep neural networks.

43, TITLE:      Relatedness Measures to Aid the Transfer of Building Blocks among Multiple Tasks
http://arxiv.org/abs/2005.03947
AUTHORS:        Trung B. Nguyen ; Will N. Browne ; Mengjie Zhang
COMMENTS:       accepted by The Genetic and Evolutionary Computation Conference (GECCO 2020)
HIGHLIGHT:      We propose a multiple-XOF system, called mXOF, that can dynamically adapt feature transfer among XOFs.

44, TITLE:      Efficiently Reusing Old Models Across Languages via Transfer Learning
http://arxiv.org/abs/1909.10955
AUTHORS:        Tom Kocmi ; Ondřej Bojar
COMMENTS:       Accepted to EAMT 2020
HIGHLIGHT:      In this paper, we propose a simple method of re-using an already trained model for different language pairs where there is no need for modifications in model architecture.

45, TITLE:      Learning Word Ratings for Empathy and Distress from Document-Level User Responses
http://arxiv.org/abs/1912.01079
AUTHORS:        João Sedoc ; Sven Buechel ; Yehonathan Nachmany ; Anneke Buffone ; Lyle Ungar
COMMENTS:       LREC 2020 camera-ready copy
HIGHLIGHT:      This paper automatically creates empathy word ratings from document-level ratings.

46, TITLE:      Ambient Sound Helps: Audiovisual Crowd Counting in Extreme Conditions
http://arxiv.org/abs/2005.07097
AUTHORS:        Di Hu ; Lichao Mou ; Qingzhong Wang ; Junyu Gao ; Yuansheng Hua ; Dejing Dou ; Xiao Xiang Zhu
HIGHLIGHT:      In this work, we introduce a novel task of audiovisual crowd counting, in which visual and auditory information are integrated for counting purposes. We collect a large-scale benchmark, named auDiovISual Crowd cOunting (DISCO) dataset, consisting of 1,935 images and the corresponding audio clips, and 170,270 annotated instances.

47, TITLE:      DoQA -- Accessing Domain-Specific FAQs via Conversational QA
http://arxiv.org/abs/2005.01328
AUTHORS:        Jon Ander Campos ; Arantxa Otegi ; Aitor Soroa ; Jan Deriu ; Mark Cieliebak ; Eneko Agirre
COMMENTS:       Accepted at ACL 2020. 13 pages 4 figures
HIGHLIGHT:      The goal of this work is to build conversational Question Answering (QA) interfaces for the large body of domain-specific information available in FAQ sites.

48, TITLE:      Evolutionary Multi-Objective Design of SARS-CoV-2 Protease Inhibitor Candidates
http://arxiv.org/abs/2005.02666
AUTHORS:        Tim Cofala ; Lars Elend ; Philip Mirbach ; Jonas Prellberg ; Thomas Teusch ; Oliver Kramer
COMMENTS:       15 pages, 7 figures, submitted to PPSN 2020
HIGHLIGHT:      We propose an evolutionary multi-objective algorithm (EMOA) to design potential protease inhibitors for SARS-CoV-2's main protease.

49, TITLE:      The Microsoft Toolkit of Multi-Task Deep Neural Networks for Natural Language Understanding
http://arxiv.org/abs/2002.07972
AUTHORS:        Xiaodong Liu ; Yu Wang ; Jianshu Ji ; Hao Cheng ; Xueyun Zhu ; Emmanuel Awa ; Pengcheng He ; Weizhu Chen ; Hoifung Poon ; Guihong Cao ; Jianfeng Gao
COMMENTS:       9 pages, 3 figures and 3 tables
HIGHLIGHT:      We present MT-DNN, an open-source natural language understanding (NLU) toolkit that makes it easy for researchers and developers to train customized deep learning models.

50, TITLE:      Deep weakly-supervised learning methods for classification and localization in histology images: a survey
http://arxiv.org/abs/1909.03354
AUTHORS:        Jérôme Rony ; Soufiane Belharbi ; Jose Dolz ; Ismail Ben Ayed ; Luke McCaffrey ; Eric Granger
COMMENTS:       44 pages, 20 figures
HIGHLIGHT:      In this survey, deep weakly-supervised learning (WSL) architectures are investigated to identify and locate diseases in histology image, without the need for pixel-level annotations.

51, TITLE:      Learning from Unlabelled Videos Using Contrastive Predictive Neural 3D Mapping
http://arxiv.org/abs/1906.03764
AUTHORS:        Adam W. Harley ; Shrinidhi K. Lakshmikanth ; Fangyu Li ; Xian Zhou ; Hsiao-Yu Fish Tung ; Katerina Fragkiadaki
HIGHLIGHT:      We propose neural 3D mapping networks, which take as input 2.5D (color and depth) video streams captured by a moving camera, and lift them to stable 3D feature maps of the scene, by disentangling the scene content from the motion of the camera.

52, TITLE:      Learning Structured Representations of Spatial and Interactive Dynamics for Trajectory Prediction in Crowded Scenes
http://arxiv.org/abs/1911.13044
AUTHORS:        Todor Davchev ; Michael Burke ; Subramanian Ramamoorthy
HIGHLIGHT:      This work proposes a method that utilises a learned model of the environment for motion prediction.

53, TITLE:      Surgical Gesture Recognition with Optical Flow only
http://arxiv.org/abs/1904.01143
AUTHORS:        Duygu Sarikaya ; Pierre Jannin
HIGHLIGHT:      In this paper, we address the open research problem of surgical gesture recognition using motion cues from video data only.

54, TITLE:      CP-NAS: Child-Parent Neural Architecture Search for Binary Neural Networks
http://arxiv.org/abs/2005.00057
AUTHORS:        Li'an Zhuo ; Baochang Zhang ; Hanlin Chen ; Linlin Yang ; Chen Chen ; Yanjun Zhu ; David Doermann
COMMENTS:       7 pages, 6 figures
HIGHLIGHT:      To this end, a Child-Parent (CP) model is introduced to a differentiable NAS to search the binarized architecture (Child) under the supervision of a full-precision model (Parent).

55, TITLE:      Adversarial Defense via Local Flatness Regularization
http://arxiv.org/abs/1910.12165
AUTHORS:        Jia Xu ; Yiming Li ; Yong Jiang ; Shu-Tao Xia
COMMENTS:       Accepted by ICIP 2020
HIGHLIGHT:      In this paper, we define the local flatness of the loss surface as the maximum value of the chosen norm of the gradient regarding to the input within a neighborhood centered on the benign sample, and discuss the relationship between the local flatness and adversarial vulnerability.

56, TITLE:      MaskAAE: Latent space optimization for Adversarial Auto-Encoders
http://arxiv.org/abs/1912.04564
AUTHORS:        Arnab Kumar Mondal ; Sankalan Pal Chowdhury ; Aravind Jayendran ; Parag Singla ; Himanshu Asnani ; Prathosh AP
COMMENTS:       To be presented at UAI 2020
HIGHLIGHT:      In this work, we hypothesise that the dimensionality of the AE model's latent space has a critical effect on the quality of generated data.

57, TITLE:      Speech Corpus of Ainu Folklore and End-to-end Speech Recognition for Ainu Language
http://arxiv.org/abs/2002.06675
AUTHORS:        Kohei Matsuura ; Sei Ueno ; Masato Mimura ; Shinsuke Sakai ; Tatsuya Kawahara
COMMENTS:       Accepted in LREC 2020
HIGHLIGHT:      In this paper, we report speech corpus development and the structure and performance of end-to-end ASR for Ainu.

58, TITLE:      CrisisBERT: a Robust Transformer for Crisis Classification and Contextual Crisis Embedding
http://arxiv.org/abs/2005.06627
AUTHORS:        Junhua Liu ; Trisha Singhal ; Lucienne T. M. Blessing ; Kristin L. Wood ; Kwan Hui Lim
HIGHLIGHT:      This work proposes CrisisBERT, an end-to-end transformer-based model for two crisis classification tasks, namely crisis detection and crisis recognition, which shows promising results across accuracy and f1 scores.

59, TITLE:      UAVid: A Semantic Segmentation Dataset for UAV Imagery
http://arxiv.org/abs/1810.10438
AUTHORS:        Ye Lyu ; George Vosselman ; Guisong Xia ; Alper Yilmaz ; Michael Ying Yang
COMMENTS:       Accepted by ISPRS Journal of Photogrammetry and Remote Sensing
HIGHLIGHT:      In this paper, we introduce our UAVid dataset, a new high-resolution UAV semantic segmentation dataset as a complement, which brings new challenges, including large scale variation, moving object recognition and temporal consistency preservation.

60, TITLE:      Variational Inference for Learning Representations of Natural Language Edits
http://arxiv.org/abs/2004.09143
AUTHORS:        Edison Marrese-Taylor ; Machel Reid ; Yutaka Matsuo
COMMENTS:       5th Workshop on Representation Learning for NLP (RepL4NLP-2020)
HIGHLIGHT:      With this in mind, we propose a novel approach that employs variational inference to learn a continuous latent space of vector representations to capture the underlying semantic information with regard to the document editing process.

61, TITLE:      Multialternative Neural Decision Processes
http://arxiv.org/abs/2005.01081
AUTHORS:        Carlo Baldassi ; Simone Cerreia-Vioglio ; Fabio Maccheroni ; Massimo Marinacci ; Marco Pirazzini
HIGHLIGHT:      We introduce an algorithmic decision process for multialternative choice that combines binary comparisons and Markovian exploration.

62, TITLE:      OptTyper: Probabilistic Type Inference by Optimising Logical and Natural Constraints
http://arxiv.org/abs/2004.00348
AUTHORS:        Irene Vlassi Pandi ; Earl T. Barr ; Andrew D. Gordon ; Charles Sutton
HIGHLIGHT:      We present a new approach to the type inference problem for dynamic languages.

63, TITLE:      RNN-T Models Fail to Generalize to Out-of-Domain Audio: Causes and Solutions
http://arxiv.org/abs/2005.03271
AUTHORS:        Chung-Cheng Chiu ; Arun Narayanan ; Wei Han ; Rohit Prabhavalkar ; Yu Zhang ; Navdeep Jaitly ; Ruoming Pang ; Tara N. Sainath ; Patrick Nguyen ; Liangliang Cao ; Yonghui Wu
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We propose two solutions: combining multiple regularization techniques during training, and using dynamic overlapping inference.

64, TITLE:      Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity
http://arxiv.org/abs/2005.07655
AUTHORS:        Steven R. Wilson ; Walid Magdy ; Barbara McGillivray ; Gareth Tyson
COMMENTS:       Accepted at The Web Science Conference 2020
HIGHLIGHT:      In this research, we study the temporal activity trends on Urban Dictionary and provide the first analysis of how this activity relates to content being discussed on a major social network: Twitter.

65, TITLE:      \emph{cm}SalGAN: RGB-D Salient Object Detection with Cross-View Generative Adversarial Networks
http://arxiv.org/abs/1912.10280
AUTHORS:        Bo Jiang ; Zitai Zhou ; Xiao Wang ; Jin Tang ; Bin Luo
COMMENTS:       Accepted by IEEE Transactions on Multimedia
HIGHLIGHT:      In this paper, we tackle this challenge by designing a novel cross-modality Saliency Generative Adversarial Network (\emph{cm}SalGAN).

66, 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.

67, TITLE:      Syntactically Look-Ahead Attention Network for Sentence Compression
http://arxiv.org/abs/2002.01145
AUTHORS:        Hidetaka Kamigaito ; Manabu Okumura
COMMENTS:       AAAI 2020
HIGHLIGHT:      To solve this problem, we propose a novel Seq2Seq model, syntactically look-ahead attention network (SLAHAN), that can generate informative summaries by explicitly tracking both dependency parent and child words during decoding and capturing important words that will be decoded in the future.

68, TITLE:      Form2Fit: Learning Shape Priors for Generalizable Assembly from Disassembly
http://arxiv.org/abs/1910.13675
AUTHORS:        Kevin Zakka ; Andy Zeng ; Johnny Lee ; Shuran Song
COMMENTS:       Code, videos, and supplemental material are available at https://form2fit.github.io/
HIGHLIGHT:      In this work, we propose to formulate the kit assembly task as a shape matching problem, where the goal is to learn a shape descriptor that establishes geometric correspondences between object surfaces and their target placement locations from visual input.

69, TITLE:      An Evaluation of Recent Neural Sequence Tagging Models in Turkish Named Entity Recognition
http://arxiv.org/abs/2005.07692
AUTHORS:        Gizem Aras ; Didem Makaroglu ; Seniz Demir ; Altan Cakir
COMMENTS:       Submitted to Expert Systems with Applications
HIGHLIGHT:      In this work, we empirically investigate the use of recent neural architectures (Bidirectional long short-term memory and Transformer-based networks) proposed for Turkish NER tagging in the same setting.

70, TITLE:      Invariance vs. Robustness Trade-Off in Neural Networks
http://arxiv.org/abs/2002.11318
AUTHORS:        Sandesh Kamath ; Amit Deshpande ; K V Subrahmanyam
COMMENTS:       Preliminary version presented in ICML 2018 Workshop on "Towards learning with limited labels: Equivariance, Invariance,and Beyond" as "Understanding Adversarial Robustness of Symmetric Networks". Updated with additional empirical evidence in Section 3.1
HIGHLIGHT:      In this paper, we show a quantitative trade-off between rotation invariance and robustness.

71, TITLE:      Bayesian Optimization on Large Graphs via a Graph Convolutional Generative Model: Application in Cardiac Model Personalization
http://arxiv.org/abs/1907.01406
AUTHORS:        Jwala Dhamala ; Sandesh Ghimire ; John L. Sapp ; B. Milan Horacek ; Linwei Wang
COMMENTS:       9 pages, 5 figures, MICCAI
HIGHLIGHT:      In this paper, we present a novel graph convolutional VAE to allow generative modeling of non-Euclidean data, and utilize it to embed Bayesian optimization of large graphs into a small latent space.

72, TITLE:      Exact and fast inversion of the approximate discrete Radon transform from partial data
http://arxiv.org/abs/1908.00887
AUTHORS:        Donsub Rim
COMMENTS:       4 pages, 1 figure
HIGHLIGHT:      We give an exact inversion formula for the approximate discrete Radon transform introduced in [Brady, SIAM J. Comput., 27(1), 107--119] that is of cost $O(N \log N)$ for a square 2D image with $N$ pixels and requires only partial data.

73, TITLE:      $P\neq NP$
http://arxiv.org/abs/2003.09791
AUTHORS:        Tianrong Lin
COMMENTS:       v8: Proof of Lemma 5.1 in Case 2 further changed; comments are welcome. arXiv admin note: text overlap with arXiv:1305.4029 by other authors. (The author note: no text overlap with arXiv:1305.4029, see reference [2] and [4], but text overlap with refs [2] and [4], the author has never read arXiv:1305.4029)
HIGHLIGHT:      The main contribution of the paper is that a series of results are obtained.

74, TITLE:      Learning Query Inseparable ELH Ontologies
http://arxiv.org/abs/1911.07229
AUTHORS:        Ana Ozaki ; Cosimo Persia ; Andrea Mazzullo
HIGHLIGHT:      We investigate the complexity of learning query inseparable ELH ontologies in a variant of Angluin's exact learning model.

75, TITLE:      On the Transferability of Knowledge among Vehicle Routing Problems by using Cellular Evolutionary Multitasking
http://arxiv.org/abs/2005.05066
AUTHORS:        Eneko Osaba ; Aritz D. Martinez ; Jesus L. Lobo ; Ibai Laña ; Javier Del Ser
COMMENTS:       8 pages, 1 figure, paper accepted for presentation in the 23rd IEEE International Conference on Intelligent Transportation Systems 2020 (IEEE ITSC 2020)
HIGHLIGHT:      The contribution of this research is twofold.

76, TITLE:      Classification of COVID-19 in chest X-ray images using DeTraC deep convolutional neural network
http://arxiv.org/abs/2003.13815
AUTHORS:        Asmaa Abbas ; Mohammed M. Abdelsamea ; Mohamed Medhat Gaber
HIGHLIGHT:      In this paper, we validate and adapt our previously developed CNN, called Decompose, Transfer, and Compose (DeTraC), for the classification of COVID-19 chest X-ray images.

77, TITLE:      COVID-CT-Dataset: A CT Scan Dataset about COVID-19
http://arxiv.org/abs/2003.13865
AUTHORS:        Jinyu Zhao ; Xuehai He ; Xingyi Yang ; Yichen Zhang ; Shanghang Zhang ; Pengtao Xie
HIGHLIGHT:      Using this dataset, we develop a joint classification and segmentation method that achieves an F1 of 0.85, an AUC of 0.95, and an accuracy of 0.83.

78, TITLE:      A Deep Factorization of Style and Structure in Fonts
http://arxiv.org/abs/1910.00748
AUTHORS:        Nikita Srivatsan ; Jonathan T. Barron ; Dan Klein ; Taylor Berg-Kirkpatrick
COMMENTS:       EMNLP 2019 (oral)
HIGHLIGHT:      We propose a deep factorization model for typographic analysis that disentangles content from style.

79, TITLE:      Deep Image Prior
http://arxiv.org/abs/1711.10925
AUTHORS:        Dmitry Ulyanov ; Andrea Vedaldi ; Victor Lempitsky
HIGHLIGHT:      In this paper, we show that, on the contrary, the structure of a generator network is sufficient to capture a great deal of low-level image statistics prior to any learning.

80, TITLE:      Arabic Offensive Language on Twitter: Analysis and Experiments
http://arxiv.org/abs/2004.02192
AUTHORS:        Hamdy Mubarak ; Ammar Rashed ; Kareem Darwish ; Younes Samih ; Ahmed Abdelali
COMMENTS:       10 pages, 6 figures, 3 tables
HIGHLIGHT:      In this paper, we focus on building effective Arabic offensive tweet detection.

81, TITLE:      Schema2QA: Answering Complex Queries on the Structured Web with a Neural Model
http://arxiv.org/abs/2001.05609
AUTHORS:        Silei Xu ; Giovanni Campagna ; Jian Li ; Monica S. Lam
HIGHLIGHT:      This paper proposes Schema2QA, an open-source toolkit that can build a Q&A skill from a database schema, requiring just a few manual annotations on each field.

82, TITLE:      Optimal Immunization Policy Using Dynamic Programming
http://arxiv.org/abs/1910.08677
AUTHORS:        Atiye Alaeddini ; Daniel Klein
HIGHLIGHT:      In this paper, we developed a framework for optimal health policy design in an uncertain dynamic setting.

83, TITLE:      Safety Augmented Value Estimation from Demonstrations (SAVED): Safe Deep Model-Based RL for Sparse Cost Robotic Tasks
http://arxiv.org/abs/1905.13402
AUTHORS:        Brijen Thananjeyan ; Ashwin Balakrishna ; Ugo Rosolia ; Felix Li ; Rowan McAllister ; Joseph E. Gonzalez ; Sergey Levine ; Francesco Borrelli ; Ken Goldberg
COMMENTS:       Robotics and Automation Letters and International Conference on Robotics and Automation 2020. First two authors contributed equally
HIGHLIGHT:      We address these issues with a new model-based reinforcement learning algorithm, Safety Augmented Value Estimation from Demonstrations (SAVED), which uses supervision that only identifies task completion and a modest set of suboptimal demonstrations to constrain exploration and learn efficiently while handling complex constraints.

84, TITLE:      Type Safety with JSON Subschema
http://arxiv.org/abs/1911.12651
AUTHORS:        Andrew Habib ; Avraham Shinnar ; Martin Hirzel ; Michael Pradel
HIGHLIGHT:      This paper presents a complementary technique: JSON subschema checking, which can be used for static type checking with JSON Schema.

85, TITLE:      PredNet and Predictive Coding: A Critical Review
http://arxiv.org/abs/1906.11902
AUTHORS:        Roshan Rane ; Edit Szügyi ; Vageesh Saxena ; André Ofner ; Sebastian Stober
HIGHLIGHT:      We design an extended model to test if conditioning future frame predictions on the action class of the video improves the model performance.

86, TITLE:      Data Manipulation: Towards Effective Instance Learning for Neural Dialogue Generation via Learning to Augment and Reweight
http://arxiv.org/abs/2004.02594
AUTHORS:        Hengyi Cai ; Hongshen Chen ; Yonghao Song ; Cheng Zhang ; Xiaofang Zhao ; Dawei Yin
COMMENTS:       To appear at ACL 2020 (long paper)
HIGHLIGHT:      In this paper, we propose a data manipulation framework to proactively reshape the data distribution towards reliable samples by augmenting and highlighting effective learning samples as well as reducing the effect of inefficient samples simultaneously.

87, TITLE:      Speech-VGG: A deep feature extractor for speech processing
http://arxiv.org/abs/1910.09909
AUTHORS:        Pierre Beckmann ; Mikolaj Kegler ; Hugues Saltini ; Milos Cernak
COMMENTS:       Submitted to InterSpeech2020
HIGHLIGHT:      Here, we introduce speechVGG, a flexible, transferable feature extractor tailored for integration with deep learning frameworks for speech processing.

88, TITLE:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
http://arxiv.org/abs/2004.11051
AUTHORS:        Yannis Kalantidis ; Laura Sevilla-Lara ; Ernest Mwebaze ; Dina Machuve ; Hamed Alemohammad ; David Guerena
COMMENTS:       14 papers accepted, 4 as oral, 10 as spotlights
HIGHLIGHT:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020

89, TITLE:      Neural CRF Model for Sentence Alignment in Text Simplification
http://arxiv.org/abs/2005.02324
AUTHORS:        Chao Jiang ; Mounica Maddela ; Wuwei Lan ; Yang Zhong ; Wei Xu
COMMENTS:       The paper has been accepted to ACL 2020
HIGHLIGHT:      We apply our CRF aligner to construct two new text simplification datasets, Newsela-Auto and Wiki-Auto, which are much larger and of better quality compared to the existing datasets. To evaluate and improve sentence alignment quality, we create two manually annotated sentence-aligned datasets from two commonly used text simplification corpora, Newsela and Wikipedia.

90, TITLE:      Annotation of Emotion Carriers in Personal Narratives
http://arxiv.org/abs/2002.12196
AUTHORS:        Aniruddha Tammewar ; Alessandra Cervone ; Eva-Maria Messner ; Giuseppe Riccardi
COMMENTS:       published in LREC 2020 http://www.lrec-conf.org/proceedings/lrec2020/pdf/2020.lrec-1.188.pdf
HIGHLIGHT:      This work proposes and evaluates an annotation model for identifying emotion carriers in spoken personal narratives.

91, TITLE:      It's Easier to Translate out of English than into it: Measuring Neural Translation Difficulty by Cross-Mutual Information
http://arxiv.org/abs/2005.02354
AUTHORS:        Emanuele Bugliarello ; Sabrina J. Mielke ; Antonios Anastasopoulos ; Ryan Cotterell ; Naoaki Okazaki
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we propose cross-mutual information (XMI): an asymmetric information-theoretic metric of machine translation difficulty that exploits the probabilistic nature of most neural machine translation models.

92, TITLE:      Inf-Net: Automatic COVID-19 Lung Infection Segmentation from CT Images
http://arxiv.org/abs/2004.14133
AUTHORS:        Deng-Ping Fan ; Tao Zhou ; Ge-Peng Ji ; Yi Zhou ; Geng Chen ; Huazhu Fu ; Jianbing Shen ; Ling Shao
COMMENTS:       To appear in IEEE TMI. The code is released in: https://github.com/DengPingFan/Inf-Net
HIGHLIGHT:      Our semi-supervised framework can improve the learning ability and achieve a higher performance.

93, TITLE:      SenseBERT: Driving Some Sense into BERT
http://arxiv.org/abs/1908.05646
AUTHORS:        Yoav Levine ; Barak Lenz ; Or Dagan ; Ori Ram ; Dan Padnos ; Or Sharir ; Shai Shalev-Shwartz ; Amnon Shashua ; Yoav Shoham
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      This paper proposes a method to employ weak-supervision directly at the word sense level.

94, TITLE:      Weakly Supervised Few-shot Object Segmentation using Co-Attention with Visual and Semantic Embeddings
http://arxiv.org/abs/2001.09540
AUTHORS:        Mennatullah Siam ; Naren Doraiswamy ; Boris N. Oreshkin ; Hengshuai Yao ; Martin Jagersand
COMMENTS:       Accepted to IJCAI'20. The first three authors listed contributed equally
HIGHLIGHT:      We propose a novel multi-modal interaction module for few-shot object segmentation that utilizes a co-attention mechanism using both visual and word embedding.

95, TITLE:      Generating Robust Supervision for Learning-Based Visual Navigation Using Hamilton-Jacobi Reachability
http://arxiv.org/abs/1912.10120
AUTHORS:        Anjian Li ; Somil Bansal ; Georgios Giovanis ; Varun Tolani ; Claire Tomlin ; Mo Chen
COMMENTS:       Learning for Dynamics and Control (L4DC) 2020
HIGHLIGHT:      In this paper, we present a novel Hamilton-Jacobi (HJ) reachability-based method to generate supervision for the CNN for waypoint prediction in an unseen environment.

96, TITLE:      Mitigating Gender Bias in Machine Learning Data Sets
http://arxiv.org/abs/2005.06898
AUTHORS:        Susan Leavy ; Gerardine Meaney ; Karen Wade ; Derek Greene
COMMENTS:       10 pages, 5 figures, 5 Tables, Presented as Bias2020 workshop (as part of the ECIR Conference) - http://bias.disim.univaq.it
HIGHLIGHT:      This paper proposes a framework for the identification of gender bias in training data for machine learning.The work draws upon gender theory and sociolinguistics to systematically indicate levels of bias in textual training data and associated neural word embedding models, thus highlighting pathways for both removing bias from training data and critically assessing its impact.

97, TITLE:      The Unstoppable Rise of Computational Linguistics in Deep Learning
http://arxiv.org/abs/2005.06420
AUTHORS:        James Henderson
COMMENTS:       13 pages. Accepted for publication at ACL 2020, in the theme track
HIGHLIGHT:      In this paper, we trace the history of neural networks applied to natural language understanding tasks, and identify key contributions which the nature of language has made to the development of neural network architectures.

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

99, TITLE:      Dense RepPoints: Representing Visual Objects with Dense Point Sets
http://arxiv.org/abs/1912.11473
AUTHORS:        Ze Yang ; Yinghao Xu ; Han Xue ; Zheng Zhang ; Raquel Urtasun ; Liwei Wang ; Stephen Lin ; Han Hu
HIGHLIGHT:      We present a new object representation, called Dense RepPoints, that utilizes a large set of points to describe an object at multiple levels, including both box level and pixel level.

100, TITLE:     Artificial neural networks in action for an automated cell-type classification of biological neural networks
http://arxiv.org/abs/1911.09977
AUTHORS:        Eirini Troullinou ; Grigorios Tsagkatakis ; Spyridon Chavlis ; Gergely Turi ; Wen-Ke Li ; Attila Losonczy ; Panagiotis Tsakalides ; Panayiota Poirazi
HIGHLIGHT:      In this work we address the problem of neuronal cell-type classification, and we employ a real-world dataset of raw neuronal activity measurements obtained with calcium imaging techniques.

101, TITLE:     Improving Noise Robustness In Speaker Identification Using A Two-Stage Attention Model
http://arxiv.org/abs/1909.11200
AUTHORS:        Yanpei Shi ; Qiang Huang ; Thomas Hain
COMMENTS:       Submitted to Interspeech2020
HIGHLIGHT:      The proposed approach is evaluated using the Voxceleb1 dataset, which aims at assessment of speaker recognition in real world situations.

102, TITLE:     Deep 1D-Convnet for accurate Parkinson disease detection and severity prediction from gait
http://arxiv.org/abs/1910.11509
AUTHORS:        Imanne El Maachi ; Guillaume-Alexandre Bilodeau ; Wassim Bouachir
COMMENTS:       Source code available at https://github.com/imanneelmaachi/Parkinson-disease-detection-and-severity-prediction-from-gait
HIGHLIGHT:      This paper proposes a novel intelligent Parkinson detection system based on deep learning techniques to analyze gait information.

103, TITLE:     Learning Curves for Deep Neural Networks: A Gaussian Field Theory Perspective
http://arxiv.org/abs/1906.05301
AUTHORS:        Omry Cohen ; Or Malka ; Zohar Ringel
HIGHLIGHT:      Leveraging these ideas and adopting a more physics-like approach, here we construct a versatile field-theory formalism for supervised deep learning, involving renormalization group, Feynmann diagrams, and replicas.

104, TITLE:     To Share or Not To Share: A Comprehensive Appraisal of Weight-Sharing
http://arxiv.org/abs/2002.04289
AUTHORS:        Aloïs Pourchot ; Alexis Ducarouge ; Olivier Sigaud
HIGHLIGHT:      In this paper, we take advantage of the \nasbench dataset to challenge the efficiency of WS on a representative search space.

105, TITLE:     TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
http://arxiv.org/abs/2005.02877
AUTHORS:        Michael Heck ; Carel van Niekerk ; Nurul Lubis ; Christian Geishauser ; Hsien-Chin Lin ; Marco Moresi ; Milica Gašić
COMMENTS:       10 pages, 6 figures, to be published in Proceedings of the 21st Annual SIGdial Meeting on Discourse and Dialogue
HIGHLIGHT:      In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.

106, TITLE:     Transitivity of Subtyping for Intersection Types
http://arxiv.org/abs/1906.09709
AUTHORS:        Jeremy G. Siek
COMMENTS:       18 pages
HIGHLIGHT:      This article develops a subtyping system in regular style that omits transitivity and provides a direct proof of transitivity, significantly reducing the length of the proof, exchanging the six lemmas for just one.

107, TITLE:     RTSeg: Real-time Semantic Segmentation Comparative Study
http://arxiv.org/abs/1803.02758
AUTHORS:        Mennatullah Siam ; Mostafa Gamal ; Moemen Abdel-Razek ; Senthil Yogamani ; Martin Jagersand
COMMENTS:       Accepted in IEEE ICIP 2018. IEEE Copyrights: Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses
HIGHLIGHT:      In this paper, we address this gap by presenting a real-time semantic segmentation benchmarking framework with a decoupled design for feature extraction and decoding methods.

108, 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.

109, TITLE:     Lane Detection in Low-light Conditions Using an Efficient Data Enhancement : Light Conditions Style Transfer
http://arxiv.org/abs/2002.01177
AUTHORS:        Tong Liu ; Zhaowei Chen ; Yi Yang ; Zehao Wu ; Haowei Li
COMMENTS:       Accepted by IV 2020
HIGHLIGHT:      In this paper, we propose a style-transfer-based data enhancement method, which uses Generative Adversarial Networks (GANs) to generate images in low-light conditions, that increases the environmental adaptability of the lane detector.

110, TITLE:     ProSelfLC: Progressive Self Label Correction for Target Revising in Label Noise
http://arxiv.org/abs/2005.03788
AUTHORS:        Xinshao Wang ; Yang Hua ; Elyor Kodirov ; Neil M. Robertson
COMMENTS:       Learning target revising, softer targets, entropy regularisation, EM algorithm. A target label distribution should define both the semantic class and similarity structure!
HIGHLIGHT:      In this work, we address robust deep learning under label noise (semi-supervised learning) from the perspective of target revising.

111, TITLE:     Emergence of functional and structural properties of the head direction system by optimization of recurrent neural networks
http://arxiv.org/abs/1912.10189
AUTHORS:        Christopher J. Cueva ; Peter Y. Wang ; Matthew Chin ; Xue-Xin Wei
COMMENTS:       International Conference on Learning Representations (ICLR) 2020
HIGHLIGHT:      Recent work suggests goal-driven training of neural networks can be used to model neural activity in the brain.

112, TITLE:     Generating Hierarchical Explanations on Text Classification via Feature Interaction Detection
http://arxiv.org/abs/2004.02015
AUTHORS:        Hanjie Chen ; Guangtao Zheng ; Yangfeng Ji
COMMENTS:       ACL 2020
HIGHLIGHT:      In this work, we build hierarchical explanations by detecting feature interactions.

113, TITLE:     VeREFINE: Integrating Object Pose Verification with Physics-guided Iterative Refinement
http://arxiv.org/abs/1909.05730
AUTHORS:        Dominik Bauer ; Timothy Patten ; Markus Vincze
COMMENTS:       Revised version
HIGHLIGHT:      In this work, we propose to integrate hypotheses verification with object pose refinement guided by physics simulation.

114, TITLE:     MathZero, The Classification Problem, and Set-Theoretic Type Theory
http://arxiv.org/abs/2005.05512
AUTHORS:        David McAllester
HIGHLIGHT:      We propose the foundation of set-theoretic dependent type theory and an objective defined in terms of the classification problem -- the problem of classifying concept instances up to isomorphism.

115, TITLE:     A multicenter study on radiomic features from T$_2$-weighted images of a customized MR pelvic phantom setting the basis for robust radiomic models in clinics
http://arxiv.org/abs/2005.06833
AUTHORS:        Linda Bianchini ; Joao Santinha ; Nuno Loução ; Mario Figueiredo ; Francesca Botta ; Daniela Origgi ; Marta Cremonesi ; Enrico Cassano ; Nikolaos Papanikolaou ; Alessandro Lascialfari
COMMENTS:       32 pages, 8 figures (7 + 1 supplemental), 8 tables (5 + 3 supplemental); Submitted to Magnetic Resonance in Medicine
HIGHLIGHT:      In this study we investigated the repeatability and reproducibility of radiomic features extracted from MRI images and provide a workflow to identify robust features.

116, TITLE:     Newsroom: A Dataset of 1.3 Million Summaries with Diverse Extractive Strategies
http://arxiv.org/abs/1804.11283
AUTHORS:        Max Grusky ; Mor Naaman ; Yoav Artzi
COMMENTS:       Proceedings of NAACL-HLT 2018 (Long Paper)
HIGHLIGHT:      We present NEWSROOM, a summarization dataset of 1.3 million articles and summaries written by authors and editors in newsrooms of 38 major news publications.

117, TITLE:     The critical locus of overparameterized neural networks
http://arxiv.org/abs/2005.04210
AUTHORS:        Y. Cooper
HIGHLIGHT:      In this paper, we work toward a better understanding of the geometry of the loss function $L$ of overparameterized feedforward neural networks.