2020.05.14.txt

==========New Papers==========
1, TITLE:       Reasoning with Latent Structure Refinement for Document-Level Relation Extraction
http://arxiv.org/abs/2005.06312
AUTHORS:        Guoshun Nan ; Zhijiang Guo ; Ivan Sekulić ; Wei Lu
COMMENTS:       To appear in the proceedings of ACL 2020 (Long paper)
HIGHLIGHT:      Unlike previous methods that may not be able to capture rich non-local interactions for inference, we propose a novel model that empowers the relational reasoning across sentences by automatically inducing the latent document-level graph.

2, TITLE:       Binarizing MobileNet via Evolution-based Searching
http://arxiv.org/abs/2005.06305
AUTHORS:        Hai Phan ; Zechun Liu ; Dang Huynh ; Marios Savvides ; Kwang-Ting Cheng ; Zhiqiang Shen
COMMENTS:       10 pages, 5 figures
HIGHLIGHT:      In this paper, we propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet, a compact network with separable depth-wise convolution.

3, TITLE:       A 28-nm Convolutional Neuromorphic Processor Enabling Online Learning with Spike-Based Retinas
http://arxiv.org/abs/2005.06318
AUTHORS:        Charlotte Frenkel ; Jean-Didier Legat ; David Bol
COMMENTS:       Accepted for presentation at the IEEE International Symposium on Circuits and Systems (ISCAS) 2020
HIGHLIGHT:      In order to leverage the data sparsity of spike-based neuromorphic retinas for adaptive edge computing and vision applications, we follow a top-down approach and propose SPOON, a 28-nm event-driven CNN (eCNN).

4, TITLE:       Multi-modal Embedding Fusion-based Recommender
http://arxiv.org/abs/2005.06331
AUTHORS:        Anna Wroblewska ; Jacek Dabrowski ; Michal Pastuszak ; Andrzej Michalowski ; Michal Daniluk ; Barbara Rychalska ; Mikolaj Wieczorek ; Sylwia Sysko-Romanczuk
COMMENTS:       7 pages, 8 figures
HIGHLIGHT:      Here, we present our system, its flexibility and performance.

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

6, TITLE:       The JuliaConnectoR: a functionally oriented interface for integrating Julia in R
http://arxiv.org/abs/2005.06334
AUTHORS:        Stefan Lenz ; Maren Hackenberg ; Harald Binder
COMMENTS:       17 pages, 1 figure, 4 tables
HIGHLIGHT:      Like many groups considering the new programming language Julia, we faced the challenge of accessing the algorithms that we develop in Julia from R. Therefore, we developed the R package JuliaConnectoR, available from the CRAN repository and GitHub (https://github.com/stefan-m-lenz/JuliaConnectoR), in particular for making advanced deep learning tools available.

7, TITLE:       Proxy Experience Replay: Federated Distillation for Distributed Reinforcement Leargning
http://arxiv.org/abs/2005.06105
AUTHORS:        Han Cha ; Jihong Park ; Hyesung Kim ; Mehdi Bennis ; Seong-Lyun Kim
COMMENTS:       8 pages, 5 figures, This paper is accepted to IEEE Intelligent Systems special issue of July/Aug 2020 - Federated Machine Learning
HIGHLIGHT:      Alternatively, this article presents a communication-efficient and privacy-preserving distributed RL framework, coined federated reinforcement distillation (FRD).

8, TITLE:       Neural Architecture Search for Gliomas Segmentation on Multimodal Magnetic Resonance Imaging
http://arxiv.org/abs/2005.06338
AUTHORS:        Feifan Wang ; Bharat Biswal
HIGHLIGHT:      In this work, we propose a neural architecture search (NAS) based solution to brain tumor segmentation tasks on multimodal volumetric MRI scans.

9, TITLE:       RISE Video Dataset: Recognizing Industrial Smoke Emissions
http://arxiv.org/abs/2005.06111
AUTHORS:        Yen-Chia Hsu ;  Ting-Hao ;  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:      Large Scale Multi-Actor Generative Dialog Modeling
http://arxiv.org/abs/2005.06114
AUTHORS:        Alex Boyd ; Raul Puri ; Mohammad Shoeybi ; Mostofa Patwary ; Bryan Catanzaro
HIGHLIGHT:      This work introduces the Generative Conversation Control model, an augmented and fine-tuned GPT-2 language model that conditions on past reference conversations to probabilistically model multi-turn conversations in the actor's persona.

11, TITLE:      INFOTABS: Inference on Tables as Semi-structured Data
http://arxiv.org/abs/2005.06117
AUTHORS:        Vivek Gupta ; Maitrey Mehta ; Pegah Nokhiz ; Vivek Srikumar
COMMENTS:       16 pages, 6 figures, 14 Tables, ACL 2020, Project Page: https://infotabs.github.io/
HIGHLIGHT:      In this paper, we observe that semi-structured tabulated text is ubiquitous; understanding them requires not only comprehending the meaning of text fragments, but also implicit relationships between them. To study this, we introduce a new dataset called INFOTABS, comprising of human-written textual hypotheses based on premises that are tables extracted from Wikipedia info-boxes.

12, TITLE:      Adversarial examples are useful too!
http://arxiv.org/abs/2005.06107
AUTHORS:        Ali Borji
HIGHLIGHT:      Here, I propose a new method to tell whether a model has been subject to a backdoor attack.

13, TITLE:      Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
http://arxiv.org/abs/2005.06123
AUTHORS:        Ming-Chang Chiu ; Tiantian Feng ; Xiang Ren ; Shrikanth Narayanan
COMMENTS:       4 pages, 3 figures, accepted to ACL NUSE workshop 2020
HIGHLIGHT:      Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues.

14, TITLE:      Towards segmentation and spatial alignment of the human embryonic brain using deep learning for atlas-based registration
http://arxiv.org/abs/2005.06368
AUTHORS:        Wietske A. P. Bastiaansen ; Melek Rousian ; Régine P. M. Steegers-Theunissen ; Wiro J. Niessen ; Anton Koning ; Stefan Klein
HIGHLIGHT:      We propose an unsupervised deep learning method for atlas based registration to achieve segmentation and spatial alignment of the embryonic brain in a single framework.

15, TITLE:      Progressive growing of self-organized hierarchical representations for exploration
http://arxiv.org/abs/2005.06369
AUTHORS:        Mayalen Etcheverry ; Pierre-Yves Oudeyer ; Chris Reinke
HIGHLIGHT:      Therefore, we present a novel technique to progressively construct a Hierarchy of Observation Latent Models for Exploration Stratification, called HOLMES.

16, TITLE:      Response-Anticipated Memory for On-Demand Knowledge Integration in Response Generation
http://arxiv.org/abs/2005.06128
AUTHORS:        Zhiliang Tian ; Wei Bi ; Dongkyu Lee ; Lanqing Xue ; Yiping Song ; Xiaojiang Liu ; Nevin L. Zhang
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      In this paper, we propose to create the document memory with some anticipated responses in mind.

17, TITLE:      Towards Hate Speech Detection at Large via Deep Generative Modeling
http://arxiv.org/abs/2005.06370
AUTHORS:        Tomer Wullach ; Amir Adler ; Einat Minkov
HIGHLIGHT:      Recently, Deep Learning (DL)-based solutions have been proposed for automatic detection of hate speech, using modest-sized training datasets of few thousands of hate speech sequences. Therefore, we first present a dataset of 1 million realistic hate and non-hate sequences, produced by a deep generative language model.

18, TITLE:      BIOMRC: A Dataset for Biomedical Machine Reading Comprehension
http://arxiv.org/abs/2005.06376
AUTHORS:        Petros Stavropoulos ; Dimitris Pappas ; Ion Androutsopoulos ; Ryan McDonald
COMMENTS:       10 pages, 4 figures, 5 tables
HIGHLIGHT:      We introduce BIOMRC, a large-scale cloze-style biomedical MRC dataset.

19, TITLE:      End-to-end Semantics-based Summary Quality Assessment for Single-document Summarization
http://arxiv.org/abs/2005.06377
AUTHORS:        Forrest Sheng Bao ; Hebi Li ; Ge Luo ; Cen Chen ; Yinfei Yang ; Minghui Qiu
HIGHLIGHT:      Therefore, we introduce a new end-to-end metric system for summary quality assessment by leveraging the semantic similarities of words and/or sentences in deep learning.

20, TITLE:      Self-Supervised Deep Visual Odometry with Online Adaptation
http://arxiv.org/abs/2005.06136
AUTHORS:        Shunkai Li ; Xin Wang ; Yingdian Cao ; Fei Xue ; Zike Yan ; Hongbin Zha
COMMENTS:       Accepted by CVPR 2020 oral
HIGHLIGHT:      In this paper, we propose an online meta-learning algorithm to enable VO networks to continuously adapt to new environments in a self-supervised manner.

21, TITLE:      Towards Interpretable Deep Learning Models for Knowledge Tracing
http://arxiv.org/abs/2005.06139
AUTHORS:        Yu Lu ; Deliang Wang ; Qinggang Meng ; Penghe Chen
HIGHLIGHT:      We thus propose to adopt the post-hoc method to tackle the interpretability issue for deep learning based knowledge tracing (DLKT) models.

22, TITLE:      SRDA-Net: Super-Resolution Domain Adaptation Networks for Semantic Segmentation
http://arxiv.org/abs/2005.06382
AUTHORS:        Enhai Liu ; Zhenjie Tang ; Bin Pan ; Xia Xu ; Tianyang Shi ; Zhenwei Shi
HIGHLIGHT:      In this work, we design a novel end-to-end semantic segmentation network, Super- Resolution Domain Adaptation Network (SRDA-Net), which could simultaneously complete super-resolution and domain adaptation.

23, TITLE:      Sanskrit Segmentation Revisited
http://arxiv.org/abs/2005.06383
AUTHORS:        Sriram Krishnan ; Amba Kulkarni
HIGHLIGHT:      Sanskrit Segmentation Revisited

24, TITLE:      Using Genetic Algorithm To Evolve Cellular Automata In Performing Edge Detection
http://arxiv.org/abs/2005.06142
AUTHORS:        Karan Nayak
COMMENTS:       5 pages
HIGHLIGHT:      In this paper we have took one such application.

25, TITLE:      3D Scene Geometry-Aware Constraint for Camera Localization with Deep Learning
http://arxiv.org/abs/2005.06147
AUTHORS:        Mi Tian ; Qiong Nie ; Hao Shen
COMMENTS:       Accepted for ICRA 2020
HIGHLIGHT:      In this work, we propose a compact network for absolute camera pose regression.

26, TITLE:      A Novel CNet-assisted Evolutionary Level Repairer and Its Applications to Super Mario Bros
http://arxiv.org/abs/2005.06148
AUTHORS:        Tianye Shu ; Ziqi Wang ; Jialin Liu ; Xin Yao
COMMENTS:       Accepted at IEEE CEC2020
HIGHLIGHT:      The proposed approaches are proved to be effective in our case study of repairing GAN-generated and artificially destroyed levels of Super Mario Bros. game.

27, TITLE:      Implicit Regularization in Deep Learning May Not Be Explainable by Norms
http://arxiv.org/abs/2005.06398
AUTHORS:        Noam Razin ; Nadav Cohen
HIGHLIGHT:      We demonstrate empirically that this interpretation extends to a certain class of non-linear neural networks, and hypothesize that it may be key to explaining generalization in deep learning.

28, TITLE:      Parallel Corpus Filtering via Pre-trained Language Models
http://arxiv.org/abs/2005.06166
AUTHORS:        Boliang Zhang ; Ajay Nagesh ; Kevin Knight
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we propose a novel approach to filter out noisy sentence pairs from web-crawled corpora via pre-trained language models.

29, TITLE:      Attribute-guided Feature Extraction and Augmentation Robust Learning for Vehicle Re-identification
http://arxiv.org/abs/2005.06184
AUTHORS:        Chaoran Zhuge ; Yujie Peng ; Yadong Li ; Jiangbo Ai ; Junru Chen
HIGHLIGHT:      In this paper, we propose a multi-guided learning approach which utilizing the information of attributes and meanwhile introducing two novel random augments to improve the robustness during training.

30, TITLE:      Context Learning for Bone Shadow Exclusion in CheXNet Accuracy Improvement
http://arxiv.org/abs/2005.06189
AUTHORS:        Minh-Chuong Huynh ; Trung-Hieu Nguyen ; Minh-Triet Tran
COMMENTS:       KSE 2018 long paper
HIGHLIGHT:      In this paper, we develop a work flow for lung disease diagnosis in chest X-ray images, which can improve the average AUROC of the state-of-the-art model from 0.8414 to 0.8445.

31, TITLE:      Mean Oriented Riesz Features for Micro Expression Classification
http://arxiv.org/abs/2005.06198
AUTHORS:        Carlos Arango Duque ; Olivier Alata ; Rémi Emonet ; Hubert Konik ; Anne-Claire Legrand
HIGHLIGHT:      Mean Oriented Riesz Features for Micro Expression Classification

32, TITLE:      Apple Defect Detection Using Deep Learning Based Object Detection For Better Post Harvest Handling
http://arxiv.org/abs/2005.06089
AUTHORS:        Paolo Valdez
COMMENTS:       Paper presented at the ICLR 2020 Workshop on Computer Vision for Agriculture (CV4A)
HIGHLIGHT:      This paper aims to help farmers with post-harvest handling by exploring if recent computer vision and deep learning methods such as the YOLOv3 (Redmon & Farhadi (2018)) can help in detecting healthy apples from apples with defects.

33, TITLE:      FaR-GAN for One-Shot Face Reenactment
http://arxiv.org/abs/2005.06402
AUTHORS:        Hanxiang Hao ; Sriram Baireddy ; Amy R. Reibman ; Edward J. Delp
COMMENTS:       This paper has been accepted to the AI for content creation workshop at CVPR 2020
HIGHLIGHT:      In this paper, we present a one-shot face reenactment model, FaR-GAN, that takes only one face image of any given source identity and a target expression as input, and then produces a face image of the same source identity but with the target expression.

34, TITLE:      Dense-Caption Matching and Frame-Selection Gating for Temporal Localization in VideoQA
http://arxiv.org/abs/2005.06409
AUTHORS:        Hyounghun Kim ; Zineng Tang ; Mohit Bansal
COMMENTS:       ACL 2020 (11 pages)
HIGHLIGHT:      In this paper, we propose a video question answering model which effectively integrates multi-modal input sources and finds the temporally relevant information to answer questions.

35, 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.

36, TITLE:      Designing a Color Filter via Optimization of Vora-Value for Making a Camera more Colorimetric
http://arxiv.org/abs/2005.06421
AUTHORS:        Yuteng Zhu ; Graham D. Finlayson
COMMENTS:       6 pages, 6 figures, 1 table, conference
HIGHLIGHT:      We argue that the Vora-Value is a suitable way to measure subspace similarity and we develop an optimization method for finding a filter that maximizes the Vora-Value measure.

37, TITLE:      Multiple Attentional Pyramid Networks for Chinese Herbal Recognition
http://arxiv.org/abs/2005.06423
AUTHORS:        Yingxue Xu ; Guihua Wen ; Yang Hu ; Mingnan Luo ; Dan Dai ; Yishan Zhuang ; Wendy Hall
COMMENTS:       14 pages, 8 figures
HIGHLIGHT:      It is expected that they can be recognized automatically using new techniques like machine learning.

38, TITLE:      Time Space Optimal Algorithm for Computing Separators in Bounded Genus Graphs
http://arxiv.org/abs/2005.06419
AUTHORS:        Chetan Gupta ; Rahul Jain ; Raghunath Tewari
HIGHLIGHT:      In this paper, we present a polynomial time algorithm that uses $O(g^{1/2}n^{1/2}\log n)$-space to find an $O(g^{1/2}n^{1/2})$-sized separator of a graph having $n$ vertices and embedded on a surface of genus $g$.

39, TITLE:      Local Fiber Orientation from X-ray Region-of-Interest Computed Tomography of large Fiber Reinforced Composite Components
http://arxiv.org/abs/2005.06431
AUTHORS:        Thomas Baranowski ; Dascha Dobrovolskij ; Kilian Dremel ; Astrid Hölzing ; Günter Lohfink ; Katja Schladitz ; Simon Zabler
HIGHLIGHT:      Here, we report on the successful combination of region-of-interest scanning with structure texture orientation analysis rendering the above described approach truly non-destructive.

40, TITLE:      Fundamentals of Computing
http://arxiv.org/abs/2005.06436
AUTHORS:        Leonid A. Levin
COMMENTS:       22 pages
HIGHLIGHT:      The notes can be used by an instructor designing a course or by students who either know the material and want to refresh the memory or are exceptionally bright and have access to an instructor for questions.

41, TITLE:      Pika parsing: parsing in reverse solves the left recursion and error recovery problems
http://arxiv.org/abs/2005.06444
AUTHORS:        Luke A. D. Hutchison
COMMENTS:       Submitted to ACM
HIGHLIGHT:      Pika parsing: parsing in reverse solves the left recursion and error recovery problems

42, TITLE:      DAugNet: Unsupervised, Multi-source, Multi-target, and Life-long Domain Adaptation for Semantic Segmentation of Satellite Images
http://arxiv.org/abs/2005.06216
AUTHORS:        Onur Tasar ; Alain Giros ; Yuliya Tarabalka ; Pierre Alliez ; Sébastien Clerc
HIGHLIGHT:      We propose a novel approach, coined DAugNet, for unsupervised, multi-source, multi-target, and life-long domain adaptation of satellite images.

43, TITLE:      On the uncertainty of self-supervised monocular depth estimation
http://arxiv.org/abs/2005.06209
AUTHORS:        Matteo Poggi ; Filippo Aleotti ; Fabio Tosi ; Stefano Mattoccia
COMMENTS:       CVPR 2020. Code will be available https://github.com/mattpoggi/mono-uncertainty
HIGHLIGHT:      Purposely, we explore for the first time how to estimate the uncertainty for this task and how this affects depth accuracy, proposing a novel peculiar technique specifically designed for self-supervised approaches.

44, TITLE:      DREAM Architecture: a Developmental Approach to Open-Ended Learning in Robotics
http://arxiv.org/abs/2005.06223
AUTHORS:        Stephane Doncieux ; Nicolas Bredeche ; Léni Le Goff ; Benoît Girard ; Alexandre Coninx ; Olivier Sigaud ; Mehdi Khamassi ; Natalia Díaz-Rodríguez ; David Filliat ; Timothy Hospedales ; A. Eiben ; Richard Duro
HIGHLIGHT:      We introduce the challenges raised by this cycle and we present DREAM (Deferred Restructuring of Experience in Autonomous Machines), a developmental cognitive architecture to bootstrap this redescription process stage by stage, build new state representations with appropriate motivations, and transfer the acquired knowledge across domains or tasks or even across robots.

45, TITLE:      Novelty Search makes Evolvability Inevitable
http://arxiv.org/abs/2005.06224
AUTHORS:        Stephane Doncieux ; Giuseppe Paolo ; Alban Laflaquière ; Alexandre Coninx
HIGHLIGHT:      Evolvability is an important feature that impacts the ability of evolutionary processes to find interesting novel solutions and to deal with changing conditions of the problem to solve.

46, TITLE:      Machine Reading Comprehension: The Role of Contextualized Language Models and Beyond
http://arxiv.org/abs/2005.06249
AUTHORS:        Zhuosheng Zhang ; Hai Zhao ; Rui Wang
COMMENTS:       51 pages
HIGHLIGHT:      In this survey, we provide a comprehensive and comparative review on MRC covering overall research topics about 1) the origin and development of MRC and CLM, with a particular focus on the role of CLMs; 2) the impact of MRC and CLM to the NLP community; 3) the definition, datasets, and evaluation of MRC; 4) general MRC architecture and technical methods in the view of two-stage Encoder-Decoder solving architecture from the insights of the cognitive process of humans; 5) previous highlights, emerging topics, and our empirical analysis, among which we especially focus on what works in different periods of MRC researches.

47, TITLE:      Mitigating Gender Bias Amplification in Distribution by Posterior Regularization
http://arxiv.org/abs/2005.06251
AUTHORS:        Shengyu Jia ; Tao Meng ; Jieyu Zhao ; Kai-Wei Chang
COMMENTS:       7 pages, 3 figures, published in ACL 2020
HIGHLIGHT:      In this paper, we investigate the gender bias amplification issue from the distribution perspective and demonstrate that the bias is amplified in the view of predicted probability distribution over labels.

48, 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 contracts as a transition system that specifies a system of automata, one automaton per party, which communicate asynchronously via crypto-speech acts, where the output of each automaton is the input of all the other automata.

49, TITLE:      Pose Proposal Critic: Robust Pose Refinement by Learning Reprojection Errors
http://arxiv.org/abs/2005.06262
AUTHORS:        Lucas Brynte ; Fredrik Kahl
HIGHLIGHT:      In this paper we focus our attention on pose refinement, and show how to push the state-of-the-art further in the case of partial occlusions.

50, TITLE:      Increased-confidence adversarial examples for improved transferability of Counter-Forensic attacks
http://arxiv.org/abs/2005.06023
AUTHORS:        Wenjie Li ; Benedetta Tondi ; Rongrong Ni ; Mauro Barni
HIGHLIGHT:      In this paper, we introduce a general strategy to increase the strength of the attacks and evaluate the transferability of the adversarial examples when such a strength varies.

51, TITLE:      Cross-Modality Relevance for Reasoning on Language and Vision
http://arxiv.org/abs/2005.06035
AUTHORS:        Chen Zheng ; Quan Guo ; Parisa Kordjamshidi
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      This work deals with the challenge of learning and reasoning over language and vision data for the related downstream tasks such as visual question answering (VQA) and natural language for visual reasoning (NLVR).

52, TITLE:      Computer Vision Toolkit for Non-invasive Monitoring of Factory Floor Artifacts
http://arxiv.org/abs/2005.06037
AUTHORS:        Aditya M. Deshpande ; Anil Kumar Telikicherla ; Vinay Jakkali ; David A. Wickelhaus ; Manish Kumar ; Sam Anand
COMMENTS:       Accepted for publication in 48th SME North American Manufacturing Research Conference (NAMRC48)
HIGHLIGHT:      This work presents a computer vision toolkit (CV Toolkit) for non-invasive digitization of the factory floor in line with Industry 4.0 requirements for factory data collection.

53, TITLE:      Generalized Multi-view Shared Subspace Learning using View Bootstrapping
http://arxiv.org/abs/2005.06038
AUTHORS:        Krishna Somandepalli ; Shrikanth Narayanan
HIGHLIGHT:      We present a neural method based on multi-view correlation to capture the information shared across a large number of views by subsampling them in a view-agnostic manner during training.

54, TITLE:      Occlusion-Adaptive Deep Network for Robust Facial Expression Recognition
http://arxiv.org/abs/2005.06040
AUTHORS:        Hui Ding ; Peng Zhou ; Rama Chellappa
HIGHLIGHT:      To further improve robustness, we propose a facial region branch to partition the feature maps into non-overlapping facial blocks and task each block to predict the expression independently.

55, TITLE:      Artificial Neural Network Pruning to Extract Knowledge
http://arxiv.org/abs/2005.06284
AUTHORS:        Evgeny M Mirkes
COMMENTS:       IJCNN 2020
HIGHLIGHT:      This paper lists the basic NN simplification problems and controlled pruning procedures to solve these problems.

56, TITLE:      Compositional Few-Shot Recognition with Primitive Discovery and Enhancing
http://arxiv.org/abs/2005.06047
AUTHORS:        Yixiong Zou ; Shanghang Zhang ; Ke Chen ; José M. F. Moura ; Yaowei Wang ; Yonghong Tian
HIGHLIGHT:      Based on this view, to imitate humans' ability of learning visual primitives and composing primitives to recognize novel classes, we propose an approach to FSL to learn a feature representation composed of important primitives, which is jointly trained with two parts, i.e. primitive discovery and primitive enhancing.

57, TITLE:      Class-Incremental Learning for Semantic Segmentation Re-Using Neither Old Data Nor Old Labels
http://arxiv.org/abs/2005.06050
AUTHORS:        Marvin Klingner ; Andreas Bär ; Philipp Donn ; Tim Fingscheidt
COMMENTS:       ITSC 2020 Conference Paper
HIGHLIGHT:      We evaluate our method on the Cityscapes dataset, where we exceed the mIoU performance of all baselines by 3.5% absolute reaching a result, which is only 2.2% absolute below the upper performance limit of single-stage training, relying on all data and labels simultaneously.

58, TITLE:      R2RML and RML Comparison for RDF Generation, their Rules Validation and Inconsistency Resolution
http://arxiv.org/abs/2005.06293
AUTHORS:        Anastasia Dimou
HIGHLIGHT:      In this paper, an overview of the state of the art on knowledge graph generation is provided, with focus on the two prevalent mapping languages: the W3C recommended R2RML and its generalisation RML.

59, TITLE:      That is a Known Lie: Detecting Previously Fact-Checked Claims
http://arxiv.org/abs/2005.06058
AUTHORS:        Shaden Shaar ; Giovanni Da San Martino ; Nikolay Babulkov ; Preslav Nakov
COMMENTS:       detecting previously fact-checked claims, fact-checking, disinformation, fake news, social media, political debates
HIGHLIGHT:      Here, we aim to bridge this gap. We further create a specialized dataset, which we release to the research community.

60, TITLE:      A computational model implementing subjectivity with the 'Room Theory'. The case of detecting Emotion from Text
http://arxiv.org/abs/2005.06059
AUTHORS:        Carlo Lipizzi ; Dario Borrelli ; Fernanda de Oliveira Capela
COMMENTS:       15 pages, 9 figures, 3 Tables - Under second round of review
HIGHLIGHT:      This work introduces a new method to consider subjectivity and general context dependency in text analysis and uses as example the detection of emotions conveyed in text.

61, TITLE:      Artificial life properties of directed interaction combinators vs. chemlambda
http://arxiv.org/abs/2005.06060
AUTHORS:        M. Buliga
HIGHLIGHT:      We provide a framework for experimentation at https://mbuliga.github.io/quinegraphs/ic-vs-chem.html#icvschem with two artificial chemistries: directed interaction combinators (dirIC, defined in section 2) and chemlambda.

62, TITLE:      Automatic Estimation of Inteligibility Measure for Consonants in Speech
http://arxiv.org/abs/2005.06065
AUTHORS:        Ali Abavisani ; Mark Hasegawa-Johnson
COMMENTS:       5 pages, 1 figure, 7 tables, submitted to Inter Speech 2020 Conference
HIGHLIGHT:      In this article, we provide a model to estimate a real-valued measure of the intelligibility of individual speech segments.

63, TITLE:      Automated Extraction of Socio-political Events from News (AESPEN): Workshop and Shared Task Report
http://arxiv.org/abs/2005.06070
AUTHORS:        Ali Hürriyetoğlu ; Vanni Zavarella ; Hristo Tanev ; Erdem Yörük ; Ali Safaya ; Osman Mutlu
HIGHLIGHT:      We describe our effort on automated extraction of socio-political events from news in the scope of a workshop and a shared task we organized at Language Resources and Evaluation Conference (LREC 2020).

64, TITLE:      Fast Deep Multi-patch Hierarchical Network for Nonhomogeneous Image Dehazing
http://arxiv.org/abs/2005.05999
AUTHORS:        Sourya Dipta Das ; Saikat Dutta
COMMENTS:       CVPR Workshops Proceedings 2020
HIGHLIGHT:      In this context, we proposed a fast Deep Multi-patch Hierarchical Network to restore Non-homogeneous hazed images by aggregating features from multiple image patches from different spatial sections of the hazed image with fewer number of network parameters.



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

2, TITLE:       Diversifying Dialogue Generation with Non-Conversational Text
http://arxiv.org/abs/2005.04346
AUTHORS:        Hui Su ; Xiaoyu Shen ; Sanqiang Zhao ; Xiao Zhou ; Pengwei Hu ; Randy Zhong ; Cheng Niu ; Jie Zhou
COMMENTS:       Accepted to ACL 2020 (long)
HIGHLIGHT:      In this paper, we propose a new perspective to diversify dialogue generation by leveraging non-conversational text. We collect a large-scale non-conversational corpus from multi sources including forum comments, idioms and book snippets.

3, TITLE:       Language Models Are An Effective Patient Representation Learning Technique For Electronic Health Record Data
http://arxiv.org/abs/2001.05295
AUTHORS:        Ethan Steinberg ; Ken Jung ; Jason A. Fries ; Conor K. Corbin ; Stephen R. Pfohl ; Nigam H. Shah
HIGHLIGHT:      Widespread adoption of electronic health records (EHRs) has fueled the development of using machine learning to build prediction models for various clinical outcomes.

4, TITLE:       Proving $μ>1$
http://arxiv.org/abs/2004.11685
AUTHORS:        Laurent Meunier ; Yann Chevaleyre ; Jeremy Rapin ; Clément W. Royer ; Olivier Teytaud
HIGHLIGHT:      In the continuous unconstrained case, we prove mathematically that $\mu=1$ leads to a sub-optimal simple regret in the case of the sphere function.

5, TITLE:       Sequential Effect Systems with Control Operators
http://arxiv.org/abs/1811.12285
AUTHORS:        Colin S. Gordon
COMMENTS:       Extended technical report corresponding to ECOOP 2020 paper "Lifting Sequential Effects to Control Operators"
HIGHLIGHT:      We address this new problem by appeal to a classic idea: macro-expression of commonly-used programming constructs in terms of control operators.

6, TITLE:       VIDIT: Virtual Image Dataset for Illumination Transfer
http://arxiv.org/abs/2005.05460
AUTHORS:        Majed El Helou ; Ruofan Zhou ; Johan Barthas ; Sabine Süsstrunk
COMMENTS:       For further information and data, see https://github.com/majedelhelou/VIDIT
HIGHLIGHT:      We present a novel dataset, the Virtual Image Dataset for Illumination Transfer (VIDIT), in an effort to create a reference evaluation benchmark and to push forward the development of illumination manipulation methods.

7, TITLE:       Jealousy-freeness and other common properties in Fair Division of Mixed Manna
http://arxiv.org/abs/2004.11469
AUTHORS:        Martin Aleksandrov
COMMENTS:       12 pages, 1 table, 2 figures
HIGHLIGHT:      For this model, we study axiomatic concepts of allocations such as jealousy-freeness up to one item, envy-freeness up to one item and Pareto-optimality.

8, TITLE:       Learning to Customize Model Structures for Few-shot Dialogue Generation Tasks
http://arxiv.org/abs/1910.14326
AUTHORS:        Yiping Song ; Zequn Liu ; Wei Bi ; Rui Yan ; Ming Zhang
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      In this paper, we propose an algorithm that can customize a unique dialogue model for each task in the few-shot setting.

9, TITLE:       Neural Polysynthetic Language Modelling
http://arxiv.org/abs/2005.05477
AUTHORS:        Lane Schwartz ; Francis Tyers ; Lori Levin ; Christo Kirov ; Patrick Littell ; Chi-kiu Lo ; Emily Prud'hommeaux ; Hyunji Hayley Park ; Kenneth Steimel ; Rebecca Knowles ; Jeffrey Micher ; Lonny Strunk ; Han Liu ; Coleman Haley ; Katherine J. Zhang ; Robbie Jimmerson ; Vasilisa Andriyanets ; Aldrian Obaja Muis ; Naoki Otani ; Jong Hyuk Park ; Zhisong Zhang
HIGHLIGHT:      When we consider polysynthetic languages (those at the extreme of morphological complexity), approaches like stemming, lemmatization, or subword modelling may not suffice.

10, TITLE:      Walking the Tightrope: An Investigation of the Convolutional Autoencoder Bottleneck
http://arxiv.org/abs/1911.07460
AUTHORS:        Ilja Manakov ; Markus Rohm ; Volker Tresp
COMMENTS:       code available at https://github.com/IljaManakov/WalkingTheTightrope
HIGHLIGHT:      In this paper, we present an in-depth investigation of the convolutional autoencoder (CAE) bottleneck.

11, TITLE:      Scalability in Perception for Autonomous Driving: Waymo Open Dataset
http://arxiv.org/abs/1912.04838
AUTHORS:        Pei Sun ; Henrik Kretzschmar ; Xerxes Dotiwalla ; Aurelien Chouard ; Vijaysai Patnaik ; Paul Tsui ; James Guo ; Yin Zhou ; Yuning Chai ; Benjamin Caine ; Vijay Vasudevan ; Wei Han ; Jiquan Ngiam ; Hang Zhao ; Aleksei Timofeev ; Scott Ettinger ; Maxim Krivokon ; Amy Gao ; Aditya Joshi ; Sheng Zhao ; Shuyang Cheng ; Yu Zhang ; Jonathon Shlens ; Zhifeng Chen ; Dragomir Anguelov
HIGHLIGHT:      In an effort to help align the research community's contributions with real-world self-driving problems, we introduce a new large scale, high quality, diverse dataset.

12, TITLE:      Mixture of Inference Networks for VAE-based Audio-visual Speech Enhancement
http://arxiv.org/abs/1912.10647
AUTHORS:        Mostafa Sadeghi ; Xavier Alameda-Pineda
HIGHLIGHT:      In this paper, we are interested in unsupervised (unknown noise) speech enhancement using latent variable generative models.

13, TITLE:      Vehicle Position Estimation with Aerial Imagery from Unmanned Aerial Vehicles
http://arxiv.org/abs/2004.08206
AUTHORS:        Friedrich Kruber ; Eduardo Sánchez Morales ; Samarjit Chakraborty ; Michael Botsch
COMMENTS:       Copyright 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:      This work describes a process to estimate a precise vehicle position from aerial imagery.

14, TITLE:      Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
http://arxiv.org/abs/2005.05050
AUTHORS:        Jian Zhan ; Joao Cartucho ; Stamatia Giannarou
COMMENTS:       7 pages, 5 figures, ICRA 2020
HIGHLIGHT:      To eliminate these assumptions, we propose a visual servoing framework for autonomous tissue scanning, able to deal with free-form tissue deformation.

15, TITLE:      On correctness of an n queens program
http://arxiv.org/abs/1909.07479
AUTHORS:        Włodzimierz Drabent
COMMENTS:       13 pages, 1 figure. This version: some corrections and improvements
HIGHLIGHT:      Another purpose of the paper is to present an example of precise declarative reasoning about the semantics of a logic program.

16, TITLE:      ENGINE: Energy-Based Inference Networks for Non-Autoregressive Machine Translation
http://arxiv.org/abs/2005.00850
AUTHORS:        Lifu Tu ; Richard Yuanzhe Pang ; Sam Wiseman ; Kevin Gimpel
COMMENTS:       ACL 2020 camera-ready version
HIGHLIGHT:      We propose to train a non-autoregressive machine translation model to minimize the energy defined by a pretrained autoregressive model.

17, TITLE:      Evolutionary Approach to Collectible Card Game Arena Deckbuilding using Active Genes
http://arxiv.org/abs/2001.01326
AUTHORS:        Jakub Kowalski ; Radosław Miernik
COMMENTS:       Accepted to IEEE Congress on Evolutionary Computation 2020
HIGHLIGHT:      In this paper, we evolve a card-choice strategy for the arena mode of Legends of Code and Magic, a programming game inspired by popular collectible card games like Hearthstone or TES: Legends.

18, TITLE:      Self-supervised Object Motion and Depth Estimation from Video
http://arxiv.org/abs/1912.04250
AUTHORS:        Qi Dai ; Vaishakh Patil ; Simon Hecker ; Dengxin Dai ; Luc Van Gool ; Konrad Schindler
COMMENTS:       Camera Ready Version for CVPRW, 2020
HIGHLIGHT:      We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video.

19, TITLE:      SpellGCN: Incorporating Phonological and Visual Similarities into Language Models for Chinese Spelling Check
http://arxiv.org/abs/2004.14166
AUTHORS:        Xingyi Cheng ; Weidi Xu ; Kunlong Chen ; Shaohua Jiang ; Feng Wang ; Taifeng Wang ; Wei Chu ; Yuan Qi
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      This paper proposes to incorporate phonological and visual similarity knowledge into language models for CSC via a specialized graph convolutional network (SpellGCN).

20, TITLE:      dMFEA-II: An Adaptive Multifactorial Evolutionary Algorithm for Permutation-based Discrete Optimization Problems
http://arxiv.org/abs/2004.06559
AUTHORS:        Eneko Osaba ; Aritz D. Martinez ; Akemi Galvez ; Andres Iglesias ; Javier Del Ser
COMMENTS:       7 pages, 0 figures, Camera-ready version of the paper accepted for presentation in The Genetic and Evolutionary Computation Conference 2020 (GECCO 2020)
HIGHLIGHT:      In this paper we entirely reformulate such concepts, making them suited to deal with permutation-based search spaces without loosing the inherent benefits of MFEA-II.

21, TITLE:      Learning the Associations of MITRE ATT&CK Adversarial Techniques
http://arxiv.org/abs/2005.01654
AUTHORS:        Rawan Al-Shaer ; Jonathan M. Spring ; Eliana Christou
COMMENTS:       13 pages, 15 figures. Pre-print / expanded version of paper accepted for publication at IEEE CNS 2020
HIGHLIGHT:      In this paper, we present our statistical machine learning analysis on APT and Software attack data reported by MITRE ATT&CK to infer the technique clustering that represents the significant correlation that can be used for technique prediction.

22, TITLE:      Document Modeling with Graph Attention Networks for Multi-grained Machine Reading Comprehension
http://arxiv.org/abs/2005.05806
AUTHORS:        Bo Zheng ; Haoyang Wen ; Yaobo Liang ; Nan Duan ; Wanxiang Che ; Daxin Jiang ; Ming Zhou ; Ting Liu
COMMENTS:       ACL2020
HIGHLIGHT:      To address this issue, we present a novel multi-grained machine reading comprehension framework that focuses on modeling documents at their hierarchical nature, which are different levels of granularity: documents, paragraphs, sentences, and tokens.

23, TITLE:      A Survey of Behavior Trees in Robotics and AI
http://arxiv.org/abs/2005.05842
AUTHORS:        Matteo Iovino ; Edvards Scukins ; Jonathan Styrud ; Petter Ögren ; Christian Smith
HIGHLIGHT:      In this paper we present a comprehensive survey of the topic of BTs in Artificial Intelligence and Robotic applications.

24, TITLE:      Balancing Training for Multilingual Neural Machine Translation
http://arxiv.org/abs/2004.06748
AUTHORS:        Xinyi Wang ; Yulia Tsvetkov ; Graham Neubig
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we propose a method that instead automatically learns how to weight training data through a data scorer that is optimized to maximize performance on all test languages.

25, TITLE:      Photometric Multi-View Mesh Refinement for High-Resolution Satellite Images
http://arxiv.org/abs/2005.04777
AUTHORS:        Mathias Rothermel ; Ke Gong ; Dieter Fritsch ; Konrad Schindler ; Norbert Haala
COMMENTS:       Accepted for publication in ISPRS Journal of Photogrammetry and Remote Sensing
HIGHLIGHT:      Here, we present an approach to recover full 3D surface meshes from multi-view satellite imagery.

26, TITLE:      Learning to Align Multi-Camera Domains using Part-Aware Clustering for Unsupervised Video Person Re-Identification
http://arxiv.org/abs/1909.13248
AUTHORS:        Youngeun Kim ; Seokeon Choi ; Taekyung Kim ; Sumin Lee ; Changick Kim
HIGHLIGHT:      In this paper, we address the scalability issue by presenting deep representation learning without ID information across multiple cameras.

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

28, TITLE:      EEG-Based Emotion Recognition Using Regularized Graph Neural Networks
http://arxiv.org/abs/1907.07835
AUTHORS:        Peixiang Zhong ; Di Wang ; Chunyan Miao
HIGHLIGHT:      In this paper, we propose a regularized graph neural network (RGNN) for EEG-based emotion recognition.

29, TITLE:      UnrealText: Synthesizing Realistic Scene Text Images from the Unreal World
http://arxiv.org/abs/2003.10608
AUTHORS:        Shangbang Long ; Cong Yao
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      In this paper, we introduce UnrealText, an efficient image synthesis method that renders realistic images via a 3D graphics engine.

30, TITLE:      Discriminative Multi-modality Speech Recognition
http://arxiv.org/abs/2005.05592
AUTHORS:        Bo Xu ; Cheng Lu ; Yandong Guo ; Jacob Wang
COMMENTS:       CVPR2020
HIGHLIGHT:      In this paper, we propose a two-stage speech recognition model.

31, TITLE:      The Creation and Detection of Deepfakes: A Survey
http://arxiv.org/abs/2004.11138
AUTHORS:        Yisroel Mirsky ; Wenke Lee
HIGHLIGHT:      In this paper, we explore the creation and detection of deepfakes and provide an in-depth view of how these architectures work.

32, TITLE:      Transforming Spectrum and Prosody for Emotional Voice Conversion with Non-Parallel Training Data
http://arxiv.org/abs/2002.00198
AUTHORS:        Kun Zhou ; Berrak Sisman ; Haizhou Li
COMMENTS:       accepted by Speaker Odyssey 2020 in Tokyo, Japan
HIGHLIGHT:      We propose a CycleGAN network to find an optimal pseudo pair from non-parallel training data by learning forward and inverse mappings simultaneously using adversarial and cycle-consistency losses.

33, TITLE:      CNN-based fast source device identification
http://arxiv.org/abs/2001.11847
AUTHORS:        Sara Mandelli ; Davide Cozzolino ; Paolo Bestagini ; Luisa Verdoliva ; Stefano Tubaro
HIGHLIGHT:      In this paper we address the problem of device identification based on sensor noise and propose a fast and accurate solution using convolutional neural networks (CNNs).

34, TITLE:      Lighthouse: Predicting Lighting Volumes for Spatially-Coherent Illumination
http://arxiv.org/abs/2003.08367
AUTHORS:        Pratul P. Srinivasan ; Ben Mildenhall ; Matthew Tancik ; Jonathan T. Barron ; Richard Tucker ; Noah Snavely
COMMENTS:       CVPR 2020. Project page: https://people.eecs.berkeley.edu/~pratul/lighthouse/ [Updates: typos corrected]
HIGHLIGHT:      We present a deep learning solution for estimating the incident illumination at any 3D location within a scene from an input narrow-baseline stereo image pair.

35, TITLE:      Learning to Perform Role-Filler Binding with Schematic Knowledge
http://arxiv.org/abs/1902.09006
AUTHORS:        Catherine Chen ; Qihong Lu ; Andre Beukers ; Christopher Baldassano ; Kenneth A. Norman
HIGHLIGHT:      In this work, we define a model as capable of performing role-filler binding if it can recall arbitrary fillers corresponding to a specified role, even when these pairings violate correlations seen during training.

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

37, TITLE:      DoorGym: A Scalable Door Opening Environment And Baseline Agent
http://arxiv.org/abs/1908.01887
AUTHORS:        Yusuke Urakami ; Alec Hodgkinson ; Casey Carlin ; Randall Leu ; Luca Rigazio ; Pieter Abbeel
COMMENTS:       Full version (Real world transfer experiments result)
HIGHLIGHT:      We introduce DoorGym, an open-source door opening simulation framework designed to utilize domain randomization to train a stable policy.

38, TITLE:      Reference Pose Generation for Visual Localization via Learned Features and View Synthesis
http://arxiv.org/abs/2005.05179
AUTHORS:        Zichao Zhang ; Torsten Sattler ; Davide Scaramuzza
COMMENTS:       23 pages, 14 figures
HIGHLIGHT:      In this work, we propose a semi-automated approach to generate reference poses based on feature matching between renderings of a 3D model and real images via learned features.

39, TITLE:      Intelligent Roundabout Insertion using Deep Reinforcement Learning
http://arxiv.org/abs/2001.00786
AUTHORS:        Alessandro Paolo Capasso ; Giulio Bacchiani ; Daniele Molinari
HIGHLIGHT:      An important topic in the autonomous driving research is the development of maneuver planning systems.

40, TITLE:      Data-driven Flood Emulation: Speeding up Urban Flood Predictions by Deep Convolutional Neural Networks
http://arxiv.org/abs/2004.08340
AUTHORS:        Zifeng Guo ; Joao P. Leitao ; Nuno E. Simoes ; Vahid Moosavi
HIGHLIGHT:      To address this issue of long computational time, this paper proposes that the prediction of maximum water depth rasters can be considered as an image-to-image translation problem where the results are generated from input elevation rasters using the information learned from data rather than by conducting simulations, which can significantly accelerate the prediction process.

41, TITLE:      NLocalSAT: Boosting Local Search with Solution Prediction
http://arxiv.org/abs/2001.09398
AUTHORS:        Wenjie Zhang ; Zeyu Sun ; Qihao Zhu ; Ge Li ; Shaowei Cai ; Yingfei Xiong ; Lu Zhang
COMMENTS:       7 pages, 3 figures
HIGHLIGHT:      To address this problem, we propose NLocalSAT.

42, TITLE:      Arbitrary Talking Face Generation via Attentional Audio-Visual Coherence Learning
http://arxiv.org/abs/1812.06589
AUTHORS:        Hao Zhu ; Huaibo Huang ; Yi Li ; Aihua Zheng ; Ran He
COMMENTS:       IJCAI-2020
HIGHLIGHT:      In this paper, we propose a novel arbitrary talking face generation framework by discovering the audio-visual coherence via the proposed Asymmetric Mutual Information Estimator (AMIE).

43, TITLE:      A Computer-Aided Diagnosis System Using Artificial Intelligence for Hip Fractures -Multi-Institutional Joint Development Research-
http://arxiv.org/abs/2003.12443
AUTHORS:        Yoichi Sato ; Yasuhiko Takegami ; Takamune Asamoto ; Yutaro Ono ; Tsugeno Hidetoshi ; Ryosuke Goto ; Akira Kitamura ; Seiwa Honda
COMMENTS:       9 pages, 4 tables, 7 figures. / author's homepage : https://www.fracture-ai.org
HIGHLIGHT:      [Conclusions] The CAD system using deep learning model which we developed was able to diagnose hip fracture in the plane X-ray with the high accuracy, and it was possible to present the decision reason.

44, TITLE:      Depth-Width Tradeoffs in Approximating Natural Functions with Neural Networks
http://arxiv.org/abs/1610.09887
AUTHORS:        Itay Safran ; Ohad Shamir
HIGHLIGHT:      We provide several new depth-based separation results for feed-forward neural networks, proving that various types of simple and natural functions can be better approximated using deeper networks than shallower ones, even if the shallower networks are much larger.

45, TITLE:      Localized convolutional neural networks for geospatial wind forecasting
http://arxiv.org/abs/2005.05930
AUTHORS:        Arnas Uselis ; Mantas Lukoševičius ; Lukas Stasytis
HIGHLIGHT:      In this work we address spatio-temporal prediction: test the effectiveness of our methods on a synthetic benchmark dataset and tackle three real-world wind prediction datasets.

46, TITLE:      BrazilDAM: A Benchmark dataset for Tailings Dam Detection
http://arxiv.org/abs/2003.07948
AUTHORS:        Edemir Ferreira ; Matheus Brito ; Remis Balaniuk ; Mário S. Alvim ; Jefersson A. dos Santos
HIGHLIGHT:      In this work we present BrazilDAM, a novel public dataset based on Sentinel-2 and Landsat-8 satellite images covering all tailings dams cataloged by the Brazilian National Mining Agency (ANM).

47, TITLE:      Tight Polynomial Worst-Case Bounds for Loop Programs
http://arxiv.org/abs/1906.10047
AUTHORS:        Amir M. Ben-Amram ; Geoff Hamilton
HIGHLIGHT:      This paper shows how to obtain asymptotically-tight, multivariate, disjunctive polynomial bounds for this class of programs.

48, TITLE:      Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset
http://arxiv.org/abs/2004.05973
AUTHORS:        Shreya Ghosh ; Abhinav Dhall ; Garima Sharma ; Sarthak Gupta ; Nicu Sebe
HIGHLIGHT:      In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.

49, TITLE:      Flowtron: an Autoregressive Flow-based Generative Network for Text-to-Speech Synthesis
http://arxiv.org/abs/2005.05957
AUTHORS:        Rafael Valle ; Kevin Shih ; Ryan Prenger ; Bryan Catanzaro
COMMENTS:       10 pages, 7 pictures
HIGHLIGHT:      In this paper we propose Flowtron: an autoregressive flow-based generative network for text-to-speech synthesis with control over speech variation and style transfer.

50, TITLE:      Multi-label classification search space in the MEKA software
http://arxiv.org/abs/1811.11353
AUTHORS:        Alex G. C. de Sá ; Gisele L. Pappa ; Alex A. Freitas
COMMENTS:       Supplementary Material (GECCO'2020): ProposedSearch Spaces
HIGHLIGHT:      Fundamentally, this occurs due to the problem transformation nature of several MLC algorithms used in this work.

51, TITLE:      jiant: A Software Toolkit for Research on General-Purpose Text Understanding Models
http://arxiv.org/abs/2003.02249
AUTHORS:        Yada Pruksachatkun ; Phil Yeres ; Haokun Liu ; Jason Phang ; Phu Mon Htut ; Alex Wang ; Ian Tenney ; Samuel R. Bowman
HIGHLIGHT:      We introduce jiant, an open source toolkit for conducting multitask and transfer learning experiments on English NLU tasks.

52, TITLE:      Introducing the VoicePrivacy Initiative
http://arxiv.org/abs/2005.01387
AUTHORS:        Natalia Tomashenko ; Brij Mohan Lal Srivastava ; Xin Wang ; Emmanuel Vincent ; Andreas Nautsch ; Junichi Yamagishi ; Nicholas Evans ; Jose Patino ; Jean-François Bonastre ; Paul-Gauthier Noé ; Massimiliano Todisco
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we formulate the voice anonymization task selected for the VoicePrivacy 2020 Challenge and describe the datasets used for system development and evaluation.

53, TITLE:      Sparse Graphical Memory for Robust Planning
http://arxiv.org/abs/2003.06417
AUTHORS:        Michael Laskin ; Scott Emmons ; Ajay Jain ; Thanard Kurutach ; Pieter Abbeel ; Deepak Pathak
COMMENTS:       Video and code at https://mishalaskin.github.io/sgm/
HIGHLIGHT:      To this end, we introduce Sparse Graphical Memory (SGM), a new data structure that stores observations and feasible transitions in a sparse memory.

54, TITLE:      Memory-Augmented Relation Network for Few-Shot Learning
http://arxiv.org/abs/2005.04414
AUTHORS:        Jun He ; Richang Hong ; Xueliang Liu ; Mingliang Xu ; Zhengjun Zha ; Meng Wang
COMMENTS:       To be submitted to ACM Multimedia 2020
HIGHLIGHT:      In this work, we investigate a new metric-learning method, Memory-Augmented Relation Network (MRN), to explicitly exploit these relationships.

55, TITLE:      Psychometric Analysis and Coupling of Emotions Between State Bulletins and Twitter in India during COVID-19 Infodemic
http://arxiv.org/abs/2005.05513
AUTHORS:        Baani Leen Kaur Jolly ; Palash Aggrawal ; Amogh Gulati ; Amarjit Singh Sethi ; Ponnurangam Kumaraguru ; Tavpritesh Sethi
HIGHLIGHT:      In this study, we analyze the psychometric impact and coupling of the COVID-19 infodemic with the official bulletins related to COVID-19 at the national and state level in India.

56, TITLE:      Controlled Crowdsourcing for High-Quality QA-SRL Annotation
http://arxiv.org/abs/1911.03243
AUTHORS:        Paul Roit ; Ayal Klein ; Daniela Stepanov ; Jonathan Mamou ; Julian Michael ; Gabriel Stanovsky ; Luke Zettlemoyer ; Ido Dagan
HIGHLIGHT:      In this paper, we present an improved crowdsourcing protocol for complex semantic annotation, involving worker selection and training, and a data consolidation phase.

57, TITLE:      Guiding Variational Response Generator to Exploit Persona
http://arxiv.org/abs/1911.02390
AUTHORS:        Bowen Wu ; Mengyuan Li ; Zongsheng Wang ; Yifu Chen ; Derek Wong ; Qihang Feng ; Junhong Huang ; Baoxun Wang
HIGHLIGHT:      Leveraging persona information of users in Neural Response Generators (NRG) to perform personalized conversations has been considered as an attractive and important topic in the research of conversational agents over the past few years.

58, TITLE:      Adaptive Mixture Regression Network with Local Counting Map for Crowd Counting
http://arxiv.org/abs/2005.05776
AUTHORS:        Xiyang Liu ; Jie Yang ; Wenrui Ding
HIGHLIGHT:      To solve this problem, we introduce a new target, named local counting map (LCM), to obtain more accurate results than density map based approaches.

59, TITLE:      DeepFaceLab: A simple, flexible and extensible face swapping framework
http://arxiv.org/abs/2005.05535
AUTHORS:        Ivan Petrov ; Daiheng Gao ; Nikolay Chervoniy ; Kunlin Liu ; Sugasa Marangonda ; Chris Umé ; Mr. Dpfks ; Luis RP ; Jian Jiang ; Sheng Zhang ; Pingyu Wu ; Bo Zhou ; Weiming Zhang
HIGHLIGHT:      In this paper, we detail the principles that drive the implementation of DeepFaceLab and introduce the pipeline of it, through which every aspect of the pipeline can be modified painlessly by users to achieve their customization purpose, and it's noteworthy that DeepFaceLab could achieve results with high fidelity and indeed indiscernible by mainstream forgery detection approaches.