2020.05.27.txt

==========New Papers==========
1, TITLE:       Active Imitation Learning with Noisy Guidance
http://arxiv.org/abs/2005.12801
AUTHORS:        Kianté Brantley ; Amr Sharaf ; Hal Daumé III
COMMENTS:       ACL 2020
HIGHLIGHT:      Imitation learning algorithms provide state-of-the-art results on many structured prediction tasks by learning near-optimal search policies.

2, TITLE:       Local Motion Planner for Autonomous Navigation in Vineyards with a RGB-D Camera-Based Algorithm and Deep Learning Synergy
http://arxiv.org/abs/2005.12815
AUTHORS:        Diego Aghi ; Vittorio Mazzia ; Marcello Chiaberge
HIGHLIGHT:      In this context, this study presents a low-cost local motion planner for autonomous navigation in vineyards based only on an RGB-D camera, low range hardware, and a dual layer control algorithm.

3, TITLE:       Predicting Entity Popularity to Improve Spoken Entity Recognition by Virtual Assistants
http://arxiv.org/abs/2005.12816
AUTHORS:        Christophe Van Gysel ; Manos Tsagkias ; Ernest Pusateri ; Ilya Oparin
COMMENTS:       SIGIR '20. The 43rd International ACM SIGIR Conference on Research & Development in Information Retrieval
HIGHLIGHT:      We introduce a method that uses historical user interactions to forecast which entities will gain in popularity and become trending, and it subsequently integrates the predictions within the Automated Speech Recognition (ASR) component of the VA.

4, TITLE:       AlphaPilot: Autonomous Drone Racing
http://arxiv.org/abs/2005.12813
AUTHORS:        Philipp Foehn ; Dario Brescianini ; Elia Kaufmann ; Titus Cieslewski ; Mathias Gehrig ; Manasi Muglikar ; Davide Scaramuzza
COMMENTS:       Accepted at Robotics: Science and Systems 2020, associated video at https://youtu.be/DGjwm5PZQT8
HIGHLIGHT:      This paper presents a novel system for autonomous, vision-based drone racing combining learned data abstraction, nonlinear filtering, and time-optimal trajectory planning.

5, TITLE:       BHN: A Brain-like Heterogeneous Network
http://arxiv.org/abs/2005.12826
AUTHORS:        Tao Liu
HIGHLIGHT:      Methods developed in this work may help to solve some key problems in pursuit of human-level intelligence.

6, TITLE:       Twitter discussions and concerns about COVID-19 pandemic: Twitter data analysis using a machine learning approach
http://arxiv.org/abs/2005.12830
AUTHORS:        Jia Xue ; Junxiang Chen ; Ran Hu ; Chen Chen ; ChengDa Zheng ; Tingshao Zhu
HIGHLIGHT:      The objective of the study is to examine coronavirus disease (COVID-19) related discussions, concerns, and sentiments that emerged from tweets posted by Twitter users.

7, TITLE:       Towards computer-aided severity assessment: training and validation of deep neural networks for geographic extent and opacity extent scoring of chest X-rays for SARS-CoV-2 lung disease severity
http://arxiv.org/abs/2005.12855
AUTHORS:        Alexander Wong ; Zhong Qiu Lin ; Linda Wang ; Audrey G. Chung ; Beiyi Shen ; Almas Abbasi ; Mahsa Hoshmand-Kochi ; Timothy Q. Duong
COMMENTS:       6 pages
HIGHLIGHT:      In this proof-of-concept study, we assess the feasibility of computer-aided scoring of CXRs of SARS-CoV-2 lung disease severity using a deep learning system.

8, TITLE:       DeepRetinotopy: Predicting the Functional Organization of Human Visual Cortex from Structural MRI Data using Geometric Deep Learning
http://arxiv.org/abs/2005.12513
AUTHORS:        Fernanda L. Ribeiro ; Steffen Bollmann ; Alexander M. Puckett
HIGHLIGHT:      Here we developed a geometric deep learning model capable of exploiting the actual structure of the cortex to learn the complex relationship between brain function and anatomy from structural and functional MRI data.

9, TITLE:       ParsBERT: Transformer-based Model for Persian Language Understanding
http://arxiv.org/abs/2005.12515
AUTHORS:        Mehrdad Farahani ; Mohammad Gharachorloo ; Marzieh Farahani ; Mohammad Manthouri
COMMENTS:       10 pages, 5 figures, 7 tables
HIGHLIGHT:      This paper proposes a monolingual BERT for the Persian language (ParsBERT), which shows its state-of-the-art performance compared to other architectures and multilingual models.

10, TITLE:      Visual Interest Prediction with Attentive Multi-Task Transfer Learning
http://arxiv.org/abs/2005.12770
AUTHORS:        Deepanway Ghosal ; Maheshkumar H. Koleka
HIGHLIGHT:      In this paper, we propose a transfer learning and attention mechanism based neural network model to predict visual interest & affective dimensions in digital photos.

11, TITLE:      Policy-Driven Neural Response Generation for Knowledge-Grounded Dialogue Systems
http://arxiv.org/abs/2005.12529
AUTHORS:        Behnam Hedayatnia ; Seokhwan Kim ; Yang Liu ; Karthik Gopalakrishnan ; Mihail Eric ; Dilek Hakkani-Tur
HIGHLIGHT:      In this paper, we propose using a dialogue policy to plan the content and style of target responses in the form of an action plan, which includes knowledge sentences related to the dialogue context, targeted dialogue acts, topic information, etc.

12, TITLE:      A New Unified Method for Detecting Text from Marathon Runners and Sports Players in Video
http://arxiv.org/abs/2005.12524
AUTHORS:        Sauradip Nag ; Palaiahnakote Shivakumara ; Umapada Pal ; Tong Lu ; Michael Blumenstein
COMMENTS:       Accepted in Pattern Recognition, Elsevier
HIGHLIGHT:      This paper presents a new unified method for tackling the above challenges.

13, TITLE:      Exploring aspects of similarity between spoken personal narratives by disentangling them into narrative clause types
http://arxiv.org/abs/2005.12762
AUTHORS:        Belen Saldias ; Deb Roy
COMMENTS:       9 pages, Proceedings of the 2020 ACL Workshop on Narrative Understanding, Storylines, and Events (NUSE). ACL
HIGHLIGHT:      We show that actions followed by the narrator's evaluation of these are the aspects non-experts consider the most. To address this challenge, we first introduce a corpus of real-world spoken personal narratives comprising 10,296 narrative clauses from 594 video transcripts.

14, TITLE:      What Are People Asking About COVID-19? A Question Classification Dataset
http://arxiv.org/abs/2005.12522
AUTHORS:        Jerry Wei ; Chengyu Huang ; Soroush Vosoughi ; Jason Wei
HIGHLIGHT:      We present COVID-Q, a set of 1,690 questions about COVID-19 from 13 sources, which we annotate into 15 question categories and 207 question classes.

15, TITLE:      Fine-Grained 3D Shape Classification with Hierarchical Part-View Attentions
http://arxiv.org/abs/2005.12541
AUTHORS:        Xinhai Liu ; Zhizhong Han ; Yu-Shen Liu ; Matthias Zwicker
COMMENTS:       The FG3D dataset is available at https://drive.google.com/drive/folders/1zLDdE8mMIxVKh3usnUhqtWm-o9TbIMdV?usp=sharing
HIGHLIGHT:      According to our experiments under this fine-grained dataset, we find that state-of-the-art methods are significantly limited by the small variance among subcategories in the same category. To address this issue, we first introduce a new dataset of fine-grained 3D shapes, which consists of three categories including airplane, car and chair.

16, TITLE:      Embedding Vector Differences Can Be Aligned With Uncertain Intensional Logic Differences
http://arxiv.org/abs/2005.12535
AUTHORS:        Ben Goertzel ; Mike Duncan ; Debbie Duong ; Nil Geisweiller ; Hedra Seid ; Abdulrahman Semrie ; Man Hin Leung ; Matthew Ikle'
HIGHLIGHT:      The DeepWalk algorithm is used to assign embedding vectors to nodes in the Atomspace weighted, labeled hypergraph that is used to represent knowledge in the OpenCog AGI system, in the context of an application to probabilistic inference regarding the causes of longevity based on data from biological ontologies and genomic analyses.

17, TITLE:      Learning a Reinforced Agent for Flexible Exposure Bracketing Selection
http://arxiv.org/abs/2005.12536
AUTHORS:        Zhouxia Wang ; Jiawei Zhang ; Mude Lin ; Jiong Wang ; Ping Luo ; Jimmy Ren
COMMENTS:       to be published in CVPR 2020
HIGHLIGHT:      Unlike previous methods that have many restrictions such as requiring camera response function, sensor noise model, and a stream of preview images with different exposures (not accessible in some scenarios e.g. some mobile applications), we propose a novel deep neural network to automatically select exposure bracketing, named EBSNet, which is sufficiently flexible without having the above restrictions. To facilitate future research, we provide a new benchmark dataset for multi-exposure selection and fusion.

18, TITLE:      Noise Robust TTS for Low Resource Speakers using Pre-trained Model and Speech Enhancement
http://arxiv.org/abs/2005.12531
AUTHORS:        Dongyang Dai ; Li Chen ; Yuping Wang ; Mu Wang ; Rui Xia ; Xuchen Song ; Zhiyong Wu ; Yuxuan Wang
HIGHLIGHT:      In this paper, the proposed end-to-end speech synthesis model uses both speaker embedding and noise representation as conditional inputs to model speaker and noise information respectively.

19, TITLE:      Guiding Symbolic Natural Language Grammar Induction via Transformer-Based Sequence Probabilities
http://arxiv.org/abs/2005.12533
AUTHORS:        Ben Goertzel ; Andres Suarez Madrigal ; Gino Yu
HIGHLIGHT:      A novel approach to automated learning of syntactic rules governing natural languages is proposed, based on using probabilities assigned to sentences (and potentially longer word sequences) by transformer neural network language models to guide symbolic learning processes like clustering and rule induction.

20, TITLE:      Keep it Simple: Image Statistics Matching for Domain Adaptation
http://arxiv.org/abs/2005.12551
AUTHORS:        Alexey Abramov ; Christopher Bayer ; Claudio Heller
HIGHLIGHT:      In this work, we focus on unsupervised DA: maintaining the detection accuracy across different data distributions, when only unlabeled images are available of the target domain.

21, TITLE:      Deepzzle: Solving Visual Jigsaw Puzzles with Deep Learning andShortest Path Optimization
http://arxiv.org/abs/2005.12548
AUTHORS:        Marie-Morgane Paumard ; David Picard ; Hedi Tabia
HIGHLIGHT:      In this paper, we notably investigate the effect of branch-cut in the graph of reassemblies.

22, TITLE:      Unsupervised Domain Expansion from Multiple Sources
http://arxiv.org/abs/2005.12544
AUTHORS:        Jing Zhang ; Wanqing Li ; Lu sheng ; Chang Tang ; Philip Ogunbona
HIGHLIGHT:      Specifically, this paper presents a method for unsupervised multi-source domain expansion (UMSDE) where only the pre-learned models of the source domains and unlabelled new domain data are available.

23, TITLE:      Learning To Classify Images Without Labels
http://arxiv.org/abs/2005.12320
AUTHORS:        Wouter Van Gansbeke ; Simon Vandenhende ; Stamatios Georgoulis ; Marc Proesmans ; Luc Van Gool
COMMENTS:       Paper + supplementary. Code + pretrained models: https://github.com/wvangansbeke/Unsupervised-Classification
HIGHLIGHT:      In this paper, we deviate from recent works, and advocate a two-step approach where feature learning and clustering are decoupled.

24, TITLE:      Learning to map between ferns with differentiable binary embedding networks
http://arxiv.org/abs/2005.12563
AUTHORS:        Max Blendowski ; Mattias P. Heinrich
HIGHLIGHT:      In this work, we present a novel concept that enables the application of differentiable random ferns in end-to-end networks.

25, TITLE:      Spoken digit classification using a spin-wave delay-line active-ring reservoir computing
http://arxiv.org/abs/2005.12557
AUTHORS:        Stuart Watt ; Mikhail Kostylev
COMMENTS:       12 pages, 3 figures
HIGHLIGHT:      As a test of general applicability, we use the recently proposed spin-wave delay line active-ring reservoir computer to perform the spoken digit recognition task.

26, TITLE:      Identity-Preserving Realistic Talking Face Generation
http://arxiv.org/abs/2005.12318
AUTHORS:        Sanjana Sinha ; Sandika Biswas ; Brojeshwar Bhowmick
COMMENTS:       Accepted in IJCNN 2020
HIGHLIGHT:      In this paper, we propose a method for identity-preserving realistic facial animation from speech.

27, TITLE:      Efficient Use of heuristics for accelerating XCS-based Policy Learning in Markov Games
http://arxiv.org/abs/2005.12553
AUTHORS:        Hao Chen ; Chang Wang ; Jian Huang ; Jianxing Gong
HIGHLIGHT:      Specifically, we propose an algorithm that can efficiently learn explainable and generalized action selection rules by taking advantages of the representation of quantitative heuristics and an opponent model with an eXtended classifier system (XCS) in zero-sum Markov games.

28, TITLE:      Optimal Learning with Excitatory and Inhibitory synapses
http://arxiv.org/abs/2005.12330
AUTHORS:        Alessandro Ingrosso
COMMENTS:       16 pages, 5 figures
HIGHLIGHT:      In this work, I study the problem of storing associations between analog signals in the presence of correlations, using methods from statistical mechanics.

29, TITLE:      Unsupervised Brain Abnormality Detection Using High Fidelity Image Reconstruction Networks
http://arxiv.org/abs/2005.12573
AUTHORS:        Kazuma Kobayashi ; Ryuichiro Hataya ; Yusuke Kurose ; Amina Bolatkan ; Mototaka Miyake ; Hirokazu Watanabe ; Masamichi Takahashi ; Naoki Mihara ; Jun Itami ; Tatsuya Harada ; Ryuji Hamamoto
HIGHLIGHT:      Here, we demonstrate a novel framework for voxel-wise abnormality detection in brain magnetic resonance imaging (MRI), which exploits an image reconstruction network based on an introspective variational autoencoder trained with a structural similarity constraint.

30, TITLE:      Bayesian Stress Testing of Models in a Classification Hierarchy
http://arxiv.org/abs/2005.12327
AUTHORS:        Bashar Awwad Shiekh Hasan ; Kate Kelly
COMMENTS:       12 pages, 8 figures, conference paper accepted in WCCI 2020
HIGHLIGHT:      In this work we propose a Bayesian framework to model the interaction amongst models in such a hierarchy.

31, TITLE:      A Data-driven Approach for Noise Reduction in Distantly Supervised Biomedical Relation Extraction
http://arxiv.org/abs/2005.12565
AUTHORS:        Saadullah Amin ; Katherine Ann Dunfield ; Anna Vechkaeva ; Günter Neumann
HIGHLIGHT:      We aim to reduce such noise by extending an entity-enriched relation classification BERT model to the problem of multiple instance learning, and defining a simple data encoding scheme that significantly reduces noise, reaching state-of-the-art performance for distantly-supervised biomedical relation extraction.

32, TITLE:      Capturing Local and Global Patterns in Procedural Content Generation via Machine Learning
http://arxiv.org/abs/2005.12579
AUTHORS:        Vanessa Volz ; Niels Justesen ; Sam Snodgrass ; Sahar Asadi ; Sami Purmonen ; Christoffer Holmgård ; Julian Togelius ; Sebastian Risi
COMMENTS:       IEEE Conference on Games 2020
HIGHLIGHT:      In this paper, we propose match-three games as a domain to test PCGML algorithms regarding their ability to generate suitable patterns.

33, TITLE:      Incidental Supervision: Moving beyond Supervised Learning
http://arxiv.org/abs/2005.12339
AUTHORS:        Dan Roth
COMMENTS:       6 pages, 1 figure. Appeared in AAAI-17
HIGHLIGHT:      This paper describes several learning paradigms that are designed to alleviate the supervision bottleneck.

34, TITLE:      GECToR -- Grammatical Error Correction: Tag, Not Rewrite
http://arxiv.org/abs/2005.12592
AUTHORS:        Kostiantyn Omelianchuk ; Vitaliy Atrasevych ; Artem Chernodub ; Oleksandr Skurzhanskyi
COMMENTS:       Accepted for publication in BEA workshop (15th Workshop on Innovative Use of NLP for Building Educational Applications; co-located with ACL)
HIGHLIGHT:      In this paper, we present a simple and efficient GEC sequence tagger using a Transformer encoder.

35, TITLE:      Verification and Validation of Convex Optimization Algorithms for Model Predictive Control
http://arxiv.org/abs/2005.12588
AUTHORS:        Raphaël Cohen ; Eric Féron ; Pierre-Loïc Garoche
HIGHLIGHT:      This article discusses the formal verification of the Ellipsoid method, a convex optimization algorithm, and its code implementation as it applies to receding horizon control.

36, TITLE:      Non-cooperative Multi-agent Systems with Exploring Agents
http://arxiv.org/abs/2005.12360
AUTHORS:        Jalal Etesami ; Christoph-Nikolas Straehle
HIGHLIGHT:      We introduce a set of conditions under which the set of equations admit a unique solution and propose two algorithms that provably provide the solution in finite and infinite time horizon scenarios.

37, TITLE:      Perceptual Extreme Super Resolution Network with Receptive Field Block
http://arxiv.org/abs/2005.12597
AUTHORS:        Taizhang Shang ; Qiuju Dai ; Shengchen Zhu ; Tong Yang ; Yandong Guo
COMMENTS:       CVPRW 2020 accepted oral, 8 pages,45 figures
HIGHLIGHT:      To tackle this difficulty, we develop a super resolution network with receptive field block based on Enhanced SRGAN.

38, TITLE:      FT Speech: Danish Parliament Speech Corpus
http://arxiv.org/abs/2005.12368
AUTHORS:        Andreas Kirkedal ; Marija Stepanović ; Barbara Plank
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      This paper introduces FT Speech, a new speech corpus created from the recorded meetings of the Danish Parliament, otherwise known as the Folketing (FT).

39, TITLE:      The Unreasonable Volatility of Neural Machine Translation Models
http://arxiv.org/abs/2005.12398
AUTHORS:        Marzieh Fadaee ; Christof Monz
COMMENTS:       Accepted to Neural Generation and Translation Workshop (WNGT) at ACL 2020
HIGHLIGHT:      We investigate the unexpected volatility of NMT models where the input is semantically and syntactically correct.

40, TITLE:      Nonmonotonic Inferences with Qualitative Conditionals based on Preferred Structures on Worlds
http://arxiv.org/abs/2005.12713
AUTHORS:        Christian Komo ; Christoph Beierle
COMMENTS:       14 pages, 3 figures
HIGHLIGHT:      Using structural information derived from the conditionals in R, we introduce the preferred structure relation on worlds.

41, TITLE:      An Effective Pipeline for a Real-world Clothes Retrieval System
http://arxiv.org/abs/2005.12739
AUTHORS:        Yang-Ho Ji ; HeeJae Jun ; Insik Kim ; Jongtack Kim ; Youngjoon Kim ; Byungsoo Ko ; Hyong-Keun Kook ; Jingeun Lee ; Sangwon Lee ; Sanghyuk Park
COMMENTS:       2nd place solution on DeepFashion2 clothes retrieval challenge in CVPR2020 workshop (CVFAD)
HIGHLIGHT:      In this paper, we propose an effective pipeline for clothes retrieval system which has sturdiness on large-scale real-world fashion data.

42, TITLE:      Learning Whole-Body Human-Robot Haptic Interaction in Social Contexts
http://arxiv.org/abs/2005.12508
AUTHORS:        Joseph Campbell ; Katsu Yamane
COMMENTS:       Accepted to ICRA 2020
HIGHLIGHT:      This paper presents a learning-from-demonstration (LfD) framework for teaching human-robot social interactions that involve whole-body haptic interaction, i.e. direct human-robot contact over the full robot body.

43, TITLE:      BEEP! Korean Corpus of Online News Comments for Toxic Speech Detection
http://arxiv.org/abs/2005.12503
AUTHORS:        Jihyung Moon ; Won Ik Cho ; Junbum Lee
COMMENTS:       To be published in SocialNLP@ACL 2020
HIGHLIGHT:      We provide benchmarks using CharCNN, BiLSTM, and BERT, where BERT achieves the highest score on all tasks.

44, TITLE:      CalliGAN: Style and Structure-aware Chinese Calligraphy Character Generator
http://arxiv.org/abs/2005.12500
AUTHORS:        Shan-Jean Wu ; Chih-Yuan Yang ; Jane Yung-jen Hsu
COMMENTS:       the work has been accepted to the AI for content creation workshop at CVPR 2020
HIGHLIGHT:      We propose a novel method of this approach by incorporating Chinese characters' component information into its model.

45, TITLE:      History-Aware Question Answering in a Blocks World Dialogue System
http://arxiv.org/abs/2005.12501
AUTHORS:        Benjamin Kane ; Georgiy Platonov ; Lenhart K. Schubert
COMMENTS:       16 pages, 4 figures
HIGHLIGHT:      In this paper, we approach the problem of spatial memory in a multi-modal spoken dialogue system capable of answering questions about interaction history in a physical blocks world setting.

46, TITLE:      Learning Local Features with Context Aggregation for Visual Localization
http://arxiv.org/abs/2005.12880
AUTHORS:        Siyu Hong ; Kunhong Li ; Yongcong Zhang ; Zhiheng Fu ; Mengyi Liu ; Yulan Guo
COMMENTS:       4 pages, 2 figures
HIGHLIGHT:      In this paper, we focus on the fusion of low-level textual information and high-level semantic context information to improve the discrimitiveness of local features.

47, TITLE:      End-to-End Object Detection with Transformers
http://arxiv.org/abs/2005.12872
AUTHORS:        Nicolas Carion ; Francisco Massa ; Gabriel Synnaeve ; Nicolas Usunier ; Alexander Kirillov ; Sergey Zagoruyko
HIGHLIGHT:      We present a new method that views object detection as a direct set prediction problem.

48, TITLE:      Modeling Penetration Testing with Reinforcement Learning Using Capture-the-Flag Challenges and Tabular Q-Learning
http://arxiv.org/abs/2005.12632
AUTHORS:        Fabio Massimo Zennaro ; Laszlo Erdodi
COMMENTS:       12 pages, 7 figures
HIGHLIGHT:      In this paper, we focus our attention on simplified penetration testing problems expressed in the form of capture the flag hacking challenges, and we apply reinforcement learning algorithms to try to solve them.

49, TITLE:      Long-Term Cloth-Changing Person Re-identification
http://arxiv.org/abs/2005.12633
AUTHORS:        Xuelin Qian ; Wenxuan Wang ; Li Zhang ; Fangrui Zhu ; Yanwei Fu ; Tao Xiang ; Yu-Gang Jiang ; Xiangyang Xue
COMMENTS:       24 pages, 10 figures, 5 tables
HIGHLIGHT:      In this work, we focus on a much more difficult yet practical setting where person matching is conducted over long-duration, e.g., over days and months and therefore inevitably under the new challenge of changing clothes.

50, TITLE:      Minimizing Supervision in Multi-label Categorization
http://arxiv.org/abs/2005.12892
AUTHORS:         Rajat ; Munender Varshney ; Pravendra Singh ; Vinay P. Namboodi
COMMENTS:       Accepted in CVPR-W 2020
HIGHLIGHT:      We treat this as a multi-label classification problem.

51, TITLE:      Refining Implicit Argument Annotation For UCCA
http://arxiv.org/abs/2005.12889
AUTHORS:        Ruixiang Cui ; Daniel Hershcovich
HIGHLIGHT:      In this paper, we design a typology for fine-grained implicit argument annotation on top of Universal Conceptual Cognitive Annotation's foundational layer (Abend and Rappoport, 2013).

52, TITLE:      Is your chatbot GDPR compliant? Open issues in agent design
http://arxiv.org/abs/2005.12644
AUTHORS:        Rahime Belen Saglam ; Jason R. C. Nurse
HIGHLIGHT:      This paper explores some of these questions, with the aim of defining relevant open issues in conversational agent design.

53, TITLE:      DAG-Net: Double Attentive Graph Neural Network for Trajectory Forecasting
http://arxiv.org/abs/2005.12661
AUTHORS:        Alessio Monti ; Alessia Bertugli ; Simone Calderara ; Rita Cucchiara
HIGHLIGHT:      We address both the aforementioned aspects by proposing a new recurrent generative model that considers both single agents' future goals and interactions between different agents.

54, TITLE:      Network Bending: Manipulating The Inner Representations of Deep Generative Models
http://arxiv.org/abs/2005.12420
AUTHORS:        Terence Broad ; Frederic Fol Leymarie ; Mick Grierson
HIGHLIGHT:      We introduce a new framework for interacting with and manipulating deep generative models that we call network bending. We present a comprehensive set of deterministic transformations that can be inserted as distinct layers into the computational graph of a trained generative neural network and applied during inference.

55, TITLE:      A Deep Learning based Fast Signed Distance Map Generation
http://arxiv.org/abs/2005.12662
AUTHORS:        Zihao Wang ; Clair Vandersteen ; Thomas Demarcy ; Dan Gnansia ; Charles Raffaelli ; Nicolas Guevara ; Hervé Delingette
HIGHLIGHT:      In this paper, we propose a learning based SDM generation neural network which is demonstrated on a tridimensional cochlea shape model parameterized by 4 shape parameters.

56, TITLE:      Analysis of the Penn Korean Universal Dependency Treebank (PKT-UD): Manual Revision to Build Robust Parsing Model in Korean
http://arxiv.org/abs/2005.12898
AUTHORS:        Tae Hwan Oh ; Ji Yoon Han ; Hyonsu Choe ; Seokwon Park ; Han He ; Jinho D. Choi ; Na-Rae Han ; Jena D. Hwang ; Hansaem Kim
COMMENTS:       Accepted by The 16th International Conference on Parsing Technologies, IWPT 2020
HIGHLIGHT:      In this paper, we first open on important issues regarding the Penn Korean Universal Treebank (PKT-UD) and address these issues by revising the entire corpus manually with the aim of producing cleaner UD annotations that are more faithful to Korean grammar.

57, TITLE:      The IMS-CUBoulder System for the SIGMORPHON 2020 Shared Task on Unsupervised Morphological Paradigm Completion
http://arxiv.org/abs/2005.12411
AUTHORS:        Manuel Mager ; Katharina Kann
HIGHLIGHT:      In this paper, we present the systems of the University of Stuttgart IMS and the University of Colorado Boulder (IMS-CUBoulder) for SIGMORPHON 2020 Task 2 on unsupervised morphological paradigm completion (Kann et al., 2020).

58, TITLE:      Racism is a Virus: Anti-Asian Hate and Counterhate in Social Media during the COVID-19 Crisis
http://arxiv.org/abs/2005.12423
AUTHORS:        Caleb Ziems ; Bing He ; Sandeep Soni ; Srijan Kumar
COMMENTS:       The COVID-HATE dataset, classifier, and demo are available at http://claws.cc.gatech.edu/covid
HIGHLIGHT:      Overall, this work presents a comprehensive overview of anti-Asian hate and counterhate content during a pandemic.

59, TITLE:      qDKT: Question-centric Deep Knowledge Tracing
http://arxiv.org/abs/2005.12442
AUTHORS:        Shashank Sonkar ; Andrew E. Waters ; Andrew S. Lan ; Phillip J. Grimaldi ; Richard G. Baraniuk
HIGHLIGHT:      To overcome this limitation we introduce qDKT, a variant of DKT that models every learner's success probability on individual questions over time.

60, TITLE:      Personalized Fashion Recommendation from Personal Social Media Data: An Item-to-Set Metric Learning Approach
http://arxiv.org/abs/2005.12439
AUTHORS:        Haitian Zheng ; Kefei Wu ; Jong-Hwi Park ; Wei Zhu ; Jiebo Luo
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      In this work, we study the problem of personalized fashion recommendation from social media data, i.e. recommending new outfits to social media users that fit their fashion preferences. To validate the effectiveness of our approach, we collect a real-world social media dataset.

61, TITLE:      SurfaceNet+: An End-to-end 3D Neural Network for Very Sparse Multi-view Stereopsis
http://arxiv.org/abs/2005.12690
AUTHORS:        Mengqi Ji ; Jinzhi Zhang ; Qionghai Dai ; Lu Fang
COMMENTS:       Accepted by IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), May 2020
HIGHLIGHT:      As another line of the solution, we present SurfaceNet+, a volumetric method to handle the 'incompleteness' and the 'inaccuracy' problems induced by a very sparse MVS setup.

62, TITLE:      MaintNet: A Collaborative Open-Source Library for Predictive Maintenance Language Resources
http://arxiv.org/abs/2005.12443
AUTHORS:        Farhad Akhbardeh ; Travis Desell ; Marcos Zampieri
HIGHLIGHT:      In order to facilitate and encourage research in this area, we have developed MaintNet, a collaborative open-source library of technical and domain-specific language datasets.

63, TITLE:      SegAttnGAN: Text to Image Generation with Segmentation Attention
http://arxiv.org/abs/2005.12444
AUTHORS:        Yuchuan Gou ; Qiancheng Wu ; Minghao Li ; Bo Gong ; Mei Han
COMMENTS:       Accepted to the AI for Content Creation Workshop at CVPR 2020
HIGHLIGHT:      In this paper, we propose a novel generative network (SegAttnGAN) that utilizes additional segmentation information for the text-to-image synthesis task.

64, TITLE:      Generating Semantically Valid Adversarial Questions for TableQA
http://arxiv.org/abs/2005.12696
AUTHORS:        Yi Zhu ; Menglin Xia ; Yiwei Zhou
HIGHLIGHT:      In this paper, we propose SAGE (Semantically valid Adversarial GEnerator), a Wasserstein sequence-to-sequence model for TableQA white-box attack.

65, TITLE:      Active Measure Reinforcement Learning for Observation Cost Minimization
http://arxiv.org/abs/2005.12697
AUTHORS:        Colin Bellinger ; Rory Coles ; Mark Crowley ; Isaac Tamblyn
COMMENTS:       Under review at NeurIPS 2020
HIGHLIGHT:      We propose the active measure RL framework (Amrl) as an initial solution to this problem where the agent learns to maximize the costed return, which we define as the discounted sum of rewards minus the sum of observation costs.

66, TITLE:      Experimental evaluation of quantum Bayesian networks on IBM QX hardware
http://arxiv.org/abs/2005.12474
AUTHORS:        Sima E. Borujeni ; Nam H. Nguyen ; Saideep Nannapaneni ; Elizabeth C. Behrman ; James E. Steck
HIGHLIGHT:      In this paper, we experimentally evaluate the performance of QBN on various IBM QX hardware against Qiskit simulator and classical analysis.

67, TITLE:      CARPe Posterum: A Convolutional Approach for Real-time Pedestrian Path Prediction
http://arxiv.org/abs/2005.12469
AUTHORS:        Matías Mendieta ; Hamed Tabkhi
HIGHLIGHT:      To this end, we propose a convolutional approach for real-time pedestrian path prediction, CARPe.

68, TITLE:      Learning Robust Feature Representations for Scene Text Detection
http://arxiv.org/abs/2005.12466
AUTHORS:        Sihwan Kim ; Taejang Park
HIGHLIGHT:      To address this issue, we will present a network architecture derived from the loss to maximize conditional log-likelihood by optimizing the lower bound with a proper approximate posterior that has shown impressive performance in several generative models.

69, TITLE:      EMT: Explicit Memory Tracker with Coarse-to-Fine Reasoning for Conversational Machine Reading
http://arxiv.org/abs/2005.12484
AUTHORS:        Yifan Gao ; Chien-Sheng Wu ; Shafiq Joty ; Caiming Xiong ; Richard Socher ; Irwin King ; Michael R. Lyu ; Steven C. H. Hoi
COMMENTS:       ACL 2020, 11 pages, 3 figures
HIGHLIGHT:      In this paper, we present a new framework of conversational machine reading that comprises a novel Explicit Memory Tracker (EMT) to track whether conditions listed in the rule text have already been satisfied to make a decision.

70, TITLE:      Region-adaptive Texture Enhancement for Detailed Person Image Synthesis
http://arxiv.org/abs/2005.12486
AUTHORS:        Lingbo Yang ; Pan Wang ; Xinfeng Zhang ; Shanshe Wang ; Zhanning Gao ; Peiran Ren ; Xuansong Xie ; Siwei Ma ; Wen Gao
COMMENTS:       Accepted in ICME 2020 oral, Recommended for TMM journal
HIGHLIGHT:      In this paper we presents RATE-Net, a novel framework for synthesizing person images with sharp texture details.

71, TITLE:      Towards Fine-grained Human Pose Transfer with Detail Replenishing Network
http://arxiv.org/abs/2005.12494
AUTHORS:        Lingbo Yang ; Pan Wang ; Chang Liu ; Zhanning Gao ; Peiran Ren ; Xinfeng Zhang ; Shanshe Wang ; Siwei Ma ; Xiansheng Hua ; Wen Gao
COMMENTS:       IEEE TIP submission
HIGHLIGHT:      Aiming towards real-world applications, we develop a more challenging yet practical HPT setting, termed as Fine-grained Human Pose Transfer (FHPT), with a higher focus on semantic fidelity and detail replenishment.



==========Updates to Previous Papers==========
1, TITLE:       Robust Image Segmentation Quality Assessment
http://arxiv.org/abs/1903.08773
AUTHORS:        Leixin Zhou ; Wenxiang Deng ; Xiaodong Wu
HIGHLIGHT:      In this paper, we propose to alleviate this problem by utilizing the difference between the input image and the reconstructed image, which is conditioned on the segmentation to be assessed, to lower the chance to overfit to the undesired image features from the original input image, and thus to increase the robustness.

2, TITLE:       Learning to Recognize Discontiguous Entities
http://arxiv.org/abs/1810.08579
AUTHORS:        Aldrian Obaja Muis ; Wei Lu
COMMENTS:       9+1 pages + 8 pages supplementary, published in EMNLP 2016. v2: fix references
HIGHLIGHT:      Motivated by a previous work, we propose to use a novel hypergraph representation to jointly encode discontiguous entities of unbounded length, which can overlap with one another.

3, TITLE:       Online Mapping and Motion Planning under Uncertainty for Safe Navigation in Unknown Environments
http://arxiv.org/abs/2004.12317
AUTHORS:        Èric Pairet ; Juan David Hernández ; Marc Carreras ; Yvan Petillot ; Morteza Lahijanian
COMMENTS:       The International Journal of Robotics Research (under review)
HIGHLIGHT:      In order to cope with these constraints, this manuscript proposes an uncertainty-based framework for mapping and planning feasible motions online with probabilistic safety-guarantees.

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

5, TITLE:       Formal derivation of Mesh Neural Networks with their Forward-Only gradient Propagation
http://arxiv.org/abs/1905.06684
AUTHORS:        Federico A. Galatolo ; Mario G. C. A. Cimino ; Gigliola Vaglini
HIGHLIGHT:      This paper proposes the Mesh Neural Network (MNN), a novel architecture which allows neurons to be connected in any topology, to efficiently route information.

6, TITLE:       A continual learning survey: Defying forgetting in classification tasks
http://arxiv.org/abs/1909.08383
AUTHORS:        Matthias De Lange ; Rahaf Aljundi ; Marc Masana ; Sarah Parisot ; Xu Jia ; Ales Leonardis ; Gregory Slabaugh ; Tinne Tuytelaars
HIGHLIGHT:      We focus on task incremental classification, where tasks arrive sequentially and are delineated by clear boundaries.

7, TITLE:       Efficient adjustment sets in causal graphical models with hidden variables
http://arxiv.org/abs/2004.10521
AUTHORS:        Ezequiel Smucler ; Facundo Sapienza ; Andrea Rotnitzky
COMMENTS:       Fixed an error in Example 2
HIGHLIGHT:      We provide polynomial time algorithms to compute the globally optimal (when it exists), optimal minimal, and optimal minimum adjustment sets.

8, TITLE:       Location-Aware Feature Selection Text Detection Network
http://arxiv.org/abs/2004.10999
AUTHORS:        Zengyuan Guo ; Zilin Wang ; Zhihui Wang ; Wanli Ouyang ; Haojie Li ; Wen Gao
COMMENTS:       10 pages, 7 figures, 5 tables
HIGHLIGHT:      In this work, we discover that one important reason to this case is that regression-based methods usually utilize a fixed feature selection way, i.e. selecting features in a single location or in neighbor regions, to predict components of the bounding box, such as the distances to the boundaries or the rotation angle.

9, TITLE:       LNL-FPC: The Linear/Non-linear Fixpoint Calculus
http://arxiv.org/abs/1906.09503
AUTHORS:        Bert Lindenhovius ; Michael Mislove ; Vladimir Zamdzhiev
COMMENTS:       Extended version of the ICFP paper "Mixed linear and non-linear recursive types" available at https://dl.acm.org/citation.cfm?doid=3352468.3341715
HIGHLIGHT:      In order to do so, we describe a new technique for solving recursive domain equations within cartesian categories by constructing the solutions over pre-embeddings.

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

11, TITLE:      Guided Uncertainty-Aware Policy Optimization: Combining Learning and Model-Based Strategies for Sample-Efficient Policy Learning
http://arxiv.org/abs/2005.10872
AUTHORS:        Michelle A. Lee ; Carlos Florensa ; Jonathan Tremblay ; Nathan Ratliff ; Animesh Garg ; Fabio Ramos ; Dieter Fox
HIGHLIGHT:      In this work, we combine the strengths of model-based methods with the flexibility of learning-based methods to obtain a general method that is able to overcome inaccuracies in the robotics perception/actuation pipeline, while requiring minimal interactions with the environment.

12, 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.

13, TITLE:      Action Space Shaping in Deep Reinforcement Learning
http://arxiv.org/abs/2004.00980
AUTHORS:        Anssi Kanervisto ; Christian Scheller ; Ville Hautamäki
COMMENTS:       To appear in IEEE Conference on Games 2020. Experiment code is available at https://github.com/Miffyli/rl-action-space-shaping
HIGHLIGHT:      In this work, we aim to gain insight on these action space modifications by conducting extensive experiments in video-game environments.

14, TITLE:      Refined Gate: A Simple and Effective Gating Mechanism for Recurrent Units
http://arxiv.org/abs/2002.11338
AUTHORS:        Zhanzhan Cheng ; Yunlu Xu ; Mingjian Cheng ; Yu Qiao ; Shiliang Pu ; Yi Niu ; Fei Wu
HIGHLIGHT:      In this paper, we propose a new gating mechanism within general gated recurrent neural networks to handle this issue.

15, TITLE:      Image Hashing by Minimizing Discrete Component-wise Wasserstein Distance
http://arxiv.org/abs/2003.00134
AUTHORS:        Khoa D. Doan ; Saurav Manchanda ; Sarkhan Badirli ; Chandan K. Reddy
HIGHLIGHT:      To address this challenge, we propose a new adversarial-autoencoder hashing approach that has a much lower sample requirement and computational cost.

16, TITLE:      "Wait, I'm Still Talking!" Predicting the Dialogue Interaction Behavior Using Imagine-Then-Arbitrate Model
http://arxiv.org/abs/2002.09616
AUTHORS:        Zehao Lin ; Xiaoming Kang ; Guodun Li ; Feng Ji ; Haiqing Chen ; Yin Zhang
HIGHLIGHT:      To address this issue, in this paper, we propose a novel Imagine-then-Arbitrate (ITA) neural dialogue model to help the agent decide whether to wait or to make a response directly.

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

18, TITLE:      VQA with no questions-answers training
http://arxiv.org/abs/1811.08481
AUTHORS:        Ben-Zion Vatashsky ; Shimon Ullman
COMMENTS:       Accepted to CVPR 2020
HIGHLIGHT:      We propose a novel method that consists of two main parts: generating a question graph representation, and an answering procedure, guided by the abstract structure of the question graph to invoke an extendable set of visual estimators.

19, TITLE:      NYTWIT: A Dataset of Novel Words in the New York Times
http://arxiv.org/abs/2003.03444
AUTHORS:        Yuval Pinter ; Cassandra L. Jacobs ; Max Bittker
HIGHLIGHT:      We present the New York Times Word Innovation Types dataset, or NYTWIT, a collection of over 2,500 novel English words published in the New York Times between November 2017 and March 2019, manually annotated for their class of novelty (such as lexical derivation, dialectal variation, blending, or compounding).

20, TITLE:      Maintaining a Library of Formal Mathematics
http://arxiv.org/abs/2004.03673
AUTHORS:        Floris van Doorn ; Gabriel Ebner ; Robert Y. Lewis
COMMENTS:       To appear in Proceedings of CICM 2020
HIGHLIGHT:      To lower the barrier of entry for contributors and to lessen the burden of reviewing contributions, we have developed a number of tools for the library which check proof developments for subtle mistakes in the code and generate documentation suited for our varied audience.

21, TITLE:      Advanced Cauchy Mutation for Differential Evolution in Numerical Optimization
http://arxiv.org/abs/1907.01095
AUTHORS:        Tae Jong Choi ; Julian Togelius ; Yun-Gyung Cheong
HIGHLIGHT:      In this paper, we propose a sigmoid based parameter control that alters the failure threshold for performing the Cauchy mutation in a time-varying schedule, which can establish a good ratio between exploration and exploitation.

22, TITLE:      Sparse Interpolation With Errors in Chebyshev Basis Beyond Redundant-Block Decoding
http://arxiv.org/abs/1912.05719
AUTHORS:        Erich L. Kaltofen ; Zhi-Hong Yang
HIGHLIGHT:      We present sparse interpolation algorithms for recovering a polynomial with $\le B$ terms from $N$ evaluations at distinct values for the variable when $\le E$ of the evaluations can be erroneous.

23, TITLE:      Real-Time Semantic Background Subtraction
http://arxiv.org/abs/2002.04993
AUTHORS:        Anthony Cioppa ; Marc Van Droogenbroeck ; Marc Braham
COMMENTS:       Accepted and Published at ICIP 2020
HIGHLIGHT:      In this paper, we present a novel background subtraction algorithm called Real-Time Semantic Background Subtraction (denoted RT-SBS) which extends SBS for real-time constrained applications while keeping similar performances.

24, 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. Updated dataset statistics, results unchanged
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.

25, 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). Updated dataset statistics, results unchanged
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.

26, TITLE:      Fast Complete Algorithm for Multiplayer Nash Equilibrium
http://arxiv.org/abs/2002.04734
AUTHORS:        Sam Ganzfried
HIGHLIGHT:      We describe a new complete algorithm for computing Nash equilibrium in multiplayer general-sum games, based on a quadratically-constrained feasibility program formulation.

27, TITLE:      AutoDiscern: Rating the Quality of Online Health Information with Hierarchical Encoder Attention-based Neural Networks
http://arxiv.org/abs/1912.12999
AUTHORS:        Laura Kinkead ; Ahmed Allam ; Michael Krauthammer
HIGHLIGHT:      We compared the performance of a traditional model (Random Forest) with that of a hierarchical encoder attention-based neural network (HEA) model using two language embeddings, BERT and BioBERT.

28, TITLE:      Learning Wavefront Coding for Extended Depth of Field Imaging
http://arxiv.org/abs/1912.13423
AUTHORS:        Ugur Akpinar ; Erdem Sahin ; Monjurul Meem ; Rajesh Menon ; Atanas Gotchev
HIGHLIGHT:      We propose a computational imaging approach for EDoF, where we employ wavefront coding via a diffractive optical element (DOE) and we achieve deblurring through a convolutional neural network.

29, TITLE:      CacheQuery: Learning Replacement Policies from Hardware Caches
http://arxiv.org/abs/1912.09770
AUTHORS:        Pepe Vila ; Pierre Ganty ; Marco Guarnieri ; Boris Köpf
COMMENTS:       17 pages, 5 tables, 5 figures
HIGHLIGHT:      We show how to infer deterministic cache replacement policies using off-the-shelf automata learning and program synthesis techniques.

30, TITLE:      Illumination adaptive person reid based on teacher-student model and adversarial training
http://arxiv.org/abs/2002.01625
AUTHORS:        Ziyue Zhang ; Richard YD Xu ; Shuai Jiang ; Yang Li ; Congzhentao Huang ; Chen Deng
COMMENTS:       Accepted by ICIP 2020
HIGHLIGHT:      To address this problem, we proposed a Two-Stream Network that can separate ReID features from lighting features to enhance ReID performance. We construct two augmented datasets by synthetically changing a set of predefined lighting conditions in two of the most popular ReID benchmarks: Market1501 and DukeMTMC-ReID.

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

32, TITLE:      A Fast and Efficient Stochastic Opposition-Based Learning for Differential Evolution in Numerical Optimization
http://arxiv.org/abs/1908.08011
AUTHORS:        Tae Jong Choi ; Julian Togelius ; Yun-Gyung Cheong
HIGHLIGHT:      In this paper, we propose an improved BetaCOBL that mitigates all the limitations.

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

34, 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:      Inspired by such capability of humans, 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.

35, TITLE:      Leveraging Sentence Similarity in Natural Language Generation: Improving Beam Search using Range Voting
http://arxiv.org/abs/1908.06288
AUTHORS:        Sebastian Borgeaud ; Guy Emerson
HIGHLIGHT:      We propose a method for natural language generation, choosing the most representative output rather than the most likely output.

36, TITLE:      Teacher-Student Framework Enhanced Multi-domain Dialogue Generation
http://arxiv.org/abs/1908.07137
AUTHORS:        Shuke Peng ; Xinjing Huang ; Zehao Lin ; Feng Ji ; Haiqing Chen ; Yin Zhang
COMMENTS:       Official Version: arXiv:2005.10450
HIGHLIGHT:      In this paper, we propose a dialogue generation model that needs no external state trackers and still benefits from human-labeled semantic data.

37, TITLE:      On the Baldwin Effect under Coevolution
http://arxiv.org/abs/2004.14827
AUTHORS:        Larry Bull
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:2004.10061, arXiv:1903.07429, arXiv:1808.03471, arXiv:1811.04073
HIGHLIGHT:      This paper considers their interaction within a coevolutionary scenario, ie, where the adaptations of one species typically affects the fitness of others.

38, 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 the Conference on Automated Knowledge Base Construction (AKBC), 2020
HIGHLIGHT:      In this work, we consider the exponentially growing subarea of genetics in cancer.

39, TITLE:      Vision-based control of a knuckle boom crane with online cable length estimation
http://arxiv.org/abs/2005.11794
AUTHORS:        Geir Ole Tysse ; Andrej Cibicik ; Olav Egeland
HIGHLIGHT:      The length of the payload cable is also estimated using a least squares technique with projection.

40, TITLE:      Evolution of Cooperative Hunting in Artificial Multi-layered Societies
http://arxiv.org/abs/2005.11580
AUTHORS:        Honglin Bao ; Wolfgang Banzhaf
COMMENTS:       29 pages, 6 figures, 5 tables. An extension of our ALife 2018 conference paper
HIGHLIGHT:      In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society.

41, TITLE:      Occluded Prohibited Items Detection: An X-ray Security Inspection Benchmark and De-occlusion Attention Module
http://arxiv.org/abs/2004.08656
AUTHORS:        Yanlu Wei ; Renshuai Tao ; Zhangjie Wu ; Yuqing Ma ; Libo Zhang ; Xianglong Liu
COMMENTS:       10 pages, 7 figures; for data and code, see https://github.com/OPIXray-author/OPIXray
HIGHLIGHT:      In this work, we contribute the first high-quality object detection dataset for security inspection, named Occluded Prohibited Items X-ray (OPIXray) image benchmark.

42, TITLE:      A Reinforced Generation of Adversarial Examples for Neural Machine Translation
http://arxiv.org/abs/1911.03677
AUTHORS:        Wei Zou ; Shujian Huang ; Jun Xie ; Xinyu Dai ; Jiajun Chen
COMMENTS:       12 pages, ACL2020
HIGHLIGHT:      Instead of collecting and analyzing bad cases using limited handcrafted error features, here we investigate this issue by generating adversarial examples via a new paradigm based on reinforcement learning.

43, TITLE:      Sparsity-Aware Deep Learning for Automatic 4D Facial Expression Recognition
http://arxiv.org/abs/2002.03157
AUTHORS:        Muzammil Behzad ; Nhat Vo ; Xiaobai Li ; Guoying Zhao
COMMENTS:       Technical backlashes were found and corrections are required
HIGHLIGHT:      In this paper, we present a sparsity-aware deep network for automatic 4D facial expression recognition (FER).

44, TITLE:      CineFilter: Unsupervised Filtering for Real Time Autonomous Camera Systems
http://arxiv.org/abs/1912.05636
AUTHORS:        Sudheer Achary ; K L Bhanu Moorthy ; Syed Ashar Javed ; Nikita Shravan ; Vineet Gandhi ; Anoop Namboodiri
HIGHLIGHT:      We propose two models, one a convex optimization based approach and another a CNN based model, both of which can exploit the temporal trends in the camera behavior.