2020.04.24.txt

==========New Papers==========
1, TITLE:       Correct Me If You Can: Learning from Error Corrections and Markings
http://arxiv.org/abs/2004.11222
AUTHORS:        Julia Kreutzer ; Nathaniel Berger ; Stefan Riezler
COMMENTS:       To appear at EAMT 2020 (Research Track)
HIGHLIGHT:      We present the first user study on annotation cost and machine learnability for the less popular annotation mode of error markings.

2, TITLE:       Simulating Anisoplanatic Turbulence by Sampling Inter-modal and Spatially Correlated Zernike Coefficients
http://arxiv.org/abs/2004.11210
AUTHORS:        Nicholas Chimitt ; Stanley H. Chan
HIGHLIGHT:      In this paper, we present a propagation-free method for simulating imaging through turbulence.

3, TITLE:       Self-Attention Attribution: Interpreting Information Interactions Inside Transformer
http://arxiv.org/abs/2004.11207
AUTHORS:        Yaru Hao ; Li Dong ; Furu Wei ; Ke Xu
COMMENTS:       11 pages
HIGHLIGHT:      In this paper, we propose a self-attention attribution algorithm to interpret the information interactions inside Transformer.

4, TITLE:       An Asymetric Cycle-Consistency Loss for Dealing with Many-to-One Mappings in Image Translation: A Study on Thigh MR Scans
http://arxiv.org/abs/2004.11001
AUTHORS:        Michael Gadermayr ; Maximilian Tschuchnig ; Dorit Merhof ; Nils Krämer ; Daniel Truhn ; Burkhard Gess
COMMENTS:       Submitted to MICCAI'20
HIGHLIGHT:      In this work, we offer a solution to improve the training process in case of many-to-one mappings by modifying the cycle-consistency loss.

5, TITLE:       Virtual SAR: A Synthetic Dataset for Deep Learning based Speckle Noise Reduction Algorithms
http://arxiv.org/abs/2004.11021
AUTHORS:        Shrey Dabhi ; Kartavya Soni ; Utkarsh Patel ; Priyanka Sharma ; Manojkumar Parmar
COMMENTS:       5 pages, 2 figures, 1 table
HIGHLIGHT:      With this paper, we propose a standard way of generating synthetic data for the training of speckle reduction algorithms and demonstrate a use-case to advance research in this domain.

6, TITLE:       SimUSR: A Simple but Strong Baseline for Unsupervised Image Super-resolution
http://arxiv.org/abs/2004.11020
AUTHORS:        Namhyuk Ahn ; Jaejun Yoo ; Kyung-Ah Sohn
HIGHLIGHT:      In this paper, we tackle a fully unsupervised super-resolution problem, i.e., neither paired images nor ground truth HR images. By allowing multiple LR images, we build a set of pseudo pairs by denoising and downsampling LR images and cast the original unsupervised problem into a supervised learning problem but in one level lower.

7, TITLE:       Dynamic Fusion Network for Multi-Domain End-to-end Task-Oriented Dialog
http://arxiv.org/abs/2004.11019
AUTHORS:        Libo Qin ; Xiao Xu ; Wanxiang Che ; Yue Zhang ; Ting Liu
COMMENTS:       ACL2020
HIGHLIGHT:      To this end, we investigate methods that can make explicit use of domain knowledge and introduce a shared-private network to learn shared and specific knowledge.

8, TITLE:       Semantically-Oriented Mutation Operator in Cartesian Genetic Programming for Evolutionary Circuit Design
http://arxiv.org/abs/2004.11018
AUTHORS:        David Hodan ; Vojtech Mrazek ; Zdenek Vasicek
COMMENTS:       Accepted for Genetic and Evolutionary Computation Conference (GECCO '20), July 8--12, 2020, Canc\'un, Mexico
HIGHLIGHT:      In this paper, we propose a semantically-oriented mutation operator (SOMO) suitable for the evolutionary design of combinational circuits.

9, TITLE:       Love, Joy, Anger, Sadness, Fear, and Surprise: SE Needs Special Kinds of AI: A Case Study on Text Mining and SE
http://arxiv.org/abs/2004.11005
AUTHORS:        Nicole Novielli ; Fabio Calefato ; Filippo Lanubile
HIGHLIGHT:      Love, Joy, Anger, Sadness, Fear, and Surprise: SE Needs Special Kinds of AI: A Case Study on Text Mining and SE

10, TITLE:      Distilling Knowledge for Fast Retrieval-based Chat-bots
http://arxiv.org/abs/2004.11045
AUTHORS:        Amir Vakili Tahami ; Kamyar Ghajar ; Azadeh Shakery
COMMENTS:       Accepted for publication in the 43rd International ACM SIGIR Conference on Research and Development in Information Retrieval (SIGIR '20)
HIGHLIGHT:      In this paper, we propose a new cross-encoder architecture and transfer knowledge from this model to a bi-encoder model using distillation.

11, TITLE:      Ensemble Generative Cleaning with Feedback Loops for Defending Adversarial Attacks
http://arxiv.org/abs/2004.11273
AUTHORS:        Jianhe Yuan ; Zhihai He
COMMENTS:       8 pages, 8 figures
HIGHLIGHT:      In this paper, we develop a new method called ensemble generative cleaning with feedback loops (EGC-FL) for effective defense of deep neural networks.

12, TITLE:      QURIOUS: Question Generation Pretraining for Text Generation
http://arxiv.org/abs/2004.11026
AUTHORS:        Shashi Narayan ; Gonçalo Simoes ; Ji Ma ; Hannah Craighead ; Ryan Mcdonald
COMMENTS:       9 pages
HIGHLIGHT:      We propose question generation as a pretraining method, which better aligns with the text generation objectives.

13, TITLE:      Location-Aware Feature Selection for Scene Text Detection
http://arxiv.org/abs/2004.10999
AUTHORS:        Zengyuan Guo ; Zilin Wang ; Zhihui Wang ; Wanli Ouyang ; Haojie Li ; Wen Gao
COMMENTS:       9 pages, 7 figures, 6 tables
HIGHLIGHT:      To address this issue, we propose a novel method called Location-Aware Feature Selection (LAFS).

14, TITLE:      Cross-ethnicity Face Anti-spoofing Recognition Challenge: A Review
http://arxiv.org/abs/2004.10998
AUTHORS:        Ajian Liu ; Xuan Li ; Jun Wan ; Sergio Escalera ; Hugo Jair Escalante ; Meysam Madadi ; Yi Jin ; Zhuoyuan Wu ; Xiaogang Yu ; Zichang Tan ; Qi Yuan ; Ruikun Yang ; Benjia Zhou ; Guodong Guo ; Stan Z. Li
COMMENTS:       18 figures, 6 tables, 12 pages
HIGHLIGHT:      This paper presents an overview of the challenge, including its design, evaluation protocol and a summary of results.

15, TITLE:      Learning to Classify Intents and Slot Labels Given a Handful of Examples
http://arxiv.org/abs/2004.10793
AUTHORS:        Jason Krone ; Yi Zhang ; Mona Diab
COMMENTS:       8 pages, 2 figures
HIGHLIGHT:      We propose a new few-shot learning task, few-shot IC/SF, to study and improve the performance of IC and SF models on classes not seen at training time in ultra low resource scenarios.

16, TITLE:      Automatic Polyp Segmentation Using Convolutional Neural Networks
http://arxiv.org/abs/2004.10792
AUTHORS:        Sara Hosseinzadeh Kassani ; Peyman Hosseinzadeh Kassani ; Michal J. Wesolowski ; Kevin A. Schneider ; Ralph Deters
HIGHLIGHT:      In this paper, we compare the performance of different deep learning architectures as feature extractors, i.e. ResNet, DenseNet, InceptionV3, InceptionResNetV2 and SE-ResNeXt in the encoder part of a U-Net architecture.

17, TITLE:      Diagram Image Retrieval using Sketch-Based Deep Learning and Transfer Learning
http://arxiv.org/abs/2004.10780
AUTHORS:        Manish Bhattarai ; Diane Oyen ; Juan Castorena ; Liping Yang ; Brendt Wohlberg
HIGHLIGHT:      This paper presents a deep learning based method for image-based search for binary patent images by taking advantage of existing large natural image repositories for image search and sketch-based methods (Sketches are not identical to diagrams, but they do share some characteristics; for example, both imagery types are gray scale (binary), composed of contours, and are lacking in texture).

18, TITLE:      Action recognition in real-world videos
http://arxiv.org/abs/2004.10774
AUTHORS:        Waqas Sultani ; Qazi Ammar Arshad ; Chen Chen
HIGHLIGHT:      In this chapter, we are using action, activity, event interchangeably.

19, TITLE:      Towards a Competitive End-to-End Speech Recognition for CHiME-6 Dinner Party Transcription
http://arxiv.org/abs/2004.10799
AUTHORS:        Andrei Andrusenko ; Aleksandr Laptev ; Ivan Medennikov
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we argue that, even in such difficult cases, some end-to-end approaches show performance close to the hybrid baseline.

20, TITLE:      Visual Commonsense Graphs: Reasoning about the Dynamic Context of a Still Image
http://arxiv.org/abs/2004.10796
AUTHORS:        Jae Sung Park ; Chandra Bhagavatula ; Roozbeh Mottaghi ; Ali Farhadi ; Yejin Choi
COMMENTS:       Project Website: http://visualcomet.xyz/
HIGHLIGHT:      We propose VisualComet, the novel framework of visual commonsense reasoning tasks to predict events that might have happened before, events that might happen next, and the intents of the people at present.

21, TITLE:      Polarized-VAE: Proximity Based Disentangled Representation Learning for Text Generation
http://arxiv.org/abs/2004.10809
AUTHORS:        Vikash Balasubramanian ; Ivan Kobyzev ; Hareesh Bahuleyan ; Ilya Shapiro ; Olga Vechtomova
COMMENTS:       Follow up to ACL 2020 submission
HIGHLIGHT:      In this work we propose polarized-VAE, a novel approach that disentangles selected attributes in the latent space based on proximity measures reflecting the similarity between data points with respect to these attributes.

22, TITLE:      Tension Space Analysis for Emergent Narrative
http://arxiv.org/abs/2004.10808
AUTHORS:        Ben Kybartas ; Clark Verbrugge ; Jonathan Lessard
COMMENTS:       14 pages, 7 figures, IEEE Transactions on Games 2020
HIGHLIGHT:      In this paper, we present a novel approach to emergent narrative using the narratological theory of possible worlds and demonstrate how the design of works in such a system can be understood through a formal means of analysis inspired by expressive range analysis.

23, TITLE:      Syntactic Structure from Deep Learning
http://arxiv.org/abs/2004.10827
AUTHORS:        Tal Linzen ; Marco Baroni
COMMENTS:       In press at Annual Reviews of Linguistics
HIGHLIGHT:      In this article, we survey representative studies of the syntactic abilities of deep networks, and discuss the broader implications that this work has for theoretical linguistics.

24, TITLE:      Revisiting the Context Window for Cross-lingual Word Embeddings
http://arxiv.org/abs/2004.10813
AUTHORS:        Ryokan Ri ; Yoshimasa Tsuruoka
COMMENTS:       ACL2020
HIGHLIGHT:      In this work, we provide a thorough evaluation, in various languages, domains, and tasks, of bilingual embeddings trained with different context windows.

25, TITLE:      ParsEL 1.0: Unsupervised Entity Linking in Persian Social Media Texts
http://arxiv.org/abs/2004.10816
AUTHORS:        Majid Asgari-Bidhendi ; Farzane Fakhrian ; Behrouz Minaei-Bidgoli
COMMENTS:       8 pages, 3 figures. ParsEL service (source code is available in github)
HIGHLIGHT:      In this paper, we propose an unsupervised Persian Entity Linking system, the first entity linking system specially focused on the Persian language, which utilizes context-dependent and context-independent features.

26, TITLE:      Preserving the Hypernym Tree of WordNet in Dense Embeddings
http://arxiv.org/abs/2004.10863
AUTHORS:        Canlin Zhang ; Xiuwen Liu
HIGHLIGHT:      In this paper, we provide a novel way to generate low-dimension (dense) vector embeddings for the noun and verb synsets in WordNet, so that the hypernym-hyponym tree structure is preserved in the embeddings.

27, TITLE:      Continual Learning of Object Instances
http://arxiv.org/abs/2004.10862
AUTHORS:        Kishan Parshotam ; Mert Kilickaya
COMMENTS:       Accepted to CVPR 2020: Workshop on Continual Learning in Computer Vision
HIGHLIGHT:      We propose continual instance learning - a method that applies the concept of continual learning to the task of distinguishing instances of the same object category.

28, TITLE:      On Bayesian Search for the Feasible Space Under Computationally Expensive Constraints
http://arxiv.org/abs/2004.11055
AUTHORS:        Alma Rahat ; Michael Wood
COMMENTS:       Submitted to Parallel Problem Solving from Nature (PPSN, 2020). Main content 12 pages, total 15 pages. 1 Figures and 2 tables. Python code for Bayesian search will be available at: http://bitbucket.org/arahat/boundary-exploration
HIGHLIGHT:      We propose a novel approach for this problem: we learn a surrogate classifier that can rapidly and accurately identify feasible solutions using only a very limited number of samples ($11n$, where $n$ is the dimension of the decision space) obviating the need for full simulations.

29, TITLE:      Learning Dialog Policies from Weak Demonstrations
http://arxiv.org/abs/2004.11054
AUTHORS:        Gabriel Gordon-Hall ; Philip John Gorinski ; Shay B. Cohen
COMMENTS:       9 pages + 2 pages references + 1 page appendices, 6 figures, 2 tables, 1 algorithm, accepted as long paper at ACL2020
HIGHLIGHT:      We introduce Reinforced Fine-tune Learning, an extension to DQfD, enabling us to overcome the domain gap between the datasets and the environment.

30, TITLE:      Edge Detection using Stationary Wavelet Transform, HMM, and EM algorithm
http://arxiv.org/abs/2004.11296
AUTHORS:        S. Anand ; K. Nagajothi ; K. Nithya
COMMENTS:       07 pages, 5 figures
HIGHLIGHT:      This paper a new edge detection technique using SWT based Hidden Markov Model (WHMM) along with the expectation-maximization (EM) algorithm is proposed.

31, TITLE:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020
http://arxiv.org/abs/2004.11051
AUTHORS:        Yannis Kalantidis ; Laura Sevilla-Lara ; Ernest Mwebaze ; Dina Machuve
HIGHLIGHT:      Proceedings of the ICLR Workshop on Computer Vision for Agriculture (CV4A) 2020

32, TITLE:      Coupling semantic and statistical techniques for dynamically enriching web ontologies
http://arxiv.org/abs/2004.11081
AUTHORS:        Mohammed Maree ; Mohammed Belkhatir
HIGHLIGHT:      In this paper we present an automatic coupled statistical/semantic framework for dynamically enriching large-scale generic ontologies from the World Wide Web.

33, TITLE:      Coupled intrinsic and extrinsic human language resource-based query expansion
http://arxiv.org/abs/2004.11083
AUTHORS:        Bhawani Selvaretnam ; Mohammed Belkhatir
HIGHLIGHT:      We present here a query expansion framework which capitalizes on both linguistic characteristics of user queries and ontology resources for query constituent encoding, expansion concept extraction and concept weighting.

34, TITLE:      Signal Level Deep Metric Learning for Multimodal One-Shot Action Recognition
http://arxiv.org/abs/2004.11085
AUTHORS:        Raphael Memmesheimer ; Nick Theisen ; Dietrich Paulus
COMMENTS:       8 pages, 5figures, 6 tables
HIGHLIGHT:      Recognizing an activity with a single reference sample using metric learning approaches is a promising field research field.

35, TITLE:      DAN: A Deformation-Aware Network for Consecutive Biomedical Image Interpolation
http://arxiv.org/abs/2004.11076
AUTHORS:        Zejin Wang ; Guoqing Li ; Xi Chen ; Hua Han
HIGHLIGHT:      To address the problem, this paper introduces a deformation-aware network to synthesize each pixel in accordance with the continuity of biological tissue.

36, TITLE:      Improved Noise and Attack Robustness for Semantic Segmentation by Using Multi-Task Training with Self-Supervised Depth Estimation
http://arxiv.org/abs/2004.11072
AUTHORS:        Marvin Klingner ; Andreas Bär ; Tim Fingscheidt
COMMENTS:       CVPR 2020 Workshop on Safe Artificial Intelligence for Automated Driving
HIGHLIGHT:      In this paper, we propose to improve robustness by a multi-task training, which extends supervised semantic segmentation by a self-supervised monocular depth estimation on unlabeled videos.

37, TITLE:      Fast Convex Relaxations using Graph Discretizations
http://arxiv.org/abs/2004.11075
AUTHORS:        Jonas Geiping ; Fjedor Gaede ; Hartmut Bauermeister ; Michael Moeller
COMMENTS:       19 pages, 10 figures
HIGHLIGHT:      Yet, applying these techniques comes with a significant computational effort, reducing their feasibility in practical applications.

38, TITLE:      Natural language technology and query expansion: issues, state-of-the-art and perspectives
http://arxiv.org/abs/2004.11093
AUTHORS:        Bhawani Selvaretnam ; Mohammed Belkhatir
HIGHLIGHT:      Users information needs are expressed in natural language and successful retrieval is very much dependent on the effective communication of the intended purpose.

39, TITLE:      Rapidly Bootstrapping a Question Answering Dataset for COVID-19
http://arxiv.org/abs/2004.11339
AUTHORS:        Raphael Tang ; Rodrigo Nogueira ; Edwin Zhang ; Nikhil Gupta ; Phuong Cam ; Kyunghyun Cho ; Jimmy Lin
HIGHLIGHT:      We present CovidQA, the beginnings of a question answering dataset specifically designed for COVID-19, built by hand from knowledge gathered from Kaggle's COVID-19 Open Research Dataset Challenge.

40, TITLE:      Self-supervised Learning for Astronomical Image Classification
http://arxiv.org/abs/2004.11336
AUTHORS:        Ana Martinazzo ; Mateus Espadoto ; Nina S. T. Hirata
HIGHLIGHT:      In this paper, we propose a technique to leverage unlabeled astronomical images to pre-train deep convolutional neural networks, in order to learn a domain-specific feature extractor which improves the results of machine learning techniques in setups with small amounts of labeled data available.

41, TITLE:      Tip the Balance: Improving Exploration of Balanced Crossover Operators by Adaptive Bias
http://arxiv.org/abs/2004.11331
AUTHORS:        Luca Manzoni ; Luca Mariot ; Eva Tuba
COMMENTS:       13 pages, 4 figures
HIGHLIGHT:      This issue has been studied in this paper by applying an adaptive bias strategy to a counter-based crossover operator that introduces unbalancedness in the offspring with a certain probability, which is decreased throughout the evolutionary process.

42, TITLE:      Adaptive Forgetting Curves for Spaced Repetition Language Learning
http://arxiv.org/abs/2004.11327
AUTHORS:        Ahmed Zaidi ; Andrew Caines ; Russell Moore ; Paula Buttery ; Andrew Rice
COMMENTS:       Artificial Intelligence for Education 2020 (AIED)
HIGHLIGHT:      In this study we explore a variety of forgetting curve models incorporating psychological and linguistic features, and we use these models to predict the probability of word recall by learners of English as a second language.

43, TITLE:      Single-View View Synthesis with Multiplane Images
http://arxiv.org/abs/2004.11364
AUTHORS:        Richard Tucker ; Noah Snavely
HIGHLIGHT:      Our method learns to predict a multiplane image directly from a single image input, and we introduce scale-invariant view synthesis for supervision, enabling us to train on online video.

44, TITLE:      Supervised Contrastive Learning
http://arxiv.org/abs/2004.11362
AUTHORS:        Prannay Khosla ; Piotr Teterwak ; Chen Wang ; Aaron Sarna ; Yonglong Tian ; Phillip Isola ; Aaron Maschinot ; Ce Liu ; Dilip Krishnan
HIGHLIGHT:      In this paper, we propose a novel training methodology that consistently outperforms cross entropy on supervised learning tasks across different architectures and data augmentations.

45, TITLE:      Human-Machine Collaboration for Democratizing Data Science
http://arxiv.org/abs/2004.11113
AUTHORS:        Clément Gautrais ; Yann Dauxais ; Stefano Teso ; Samuel Kolb ; Gust Verbruggen ; Luc De Raedt
COMMENTS:       26 pages
HIGHLIGHT:      Motivated by this observation we introduce a novel framework and system \textsc{VisualSynth} for human-machine collaboration in data science.

46, TITLE:      Evaluating Adversarial Robustness for Deep Neural Network Interpretability using fMRI Decoding
http://arxiv.org/abs/2004.11114
AUTHORS:        Patrick McClure ; Dustin Moraczewski ; Ka Chun Lam ; Adam Thomas ; Francisco Pereira
HIGHLIGHT:      A saliency map is a common approach for producing interpretable visualizations of the relative importance of input features for a prediction.

47, TITLE:      Model-Based Meta-Reinforcement Learning for Flight with Suspended Payloads
http://arxiv.org/abs/2004.11345
AUTHORS:        Suneel Belkhale ; Rachel Li ; Gregory Kahn ; Rowan McAllister ; Roberto Calandra ; Sergey Levine
HIGHLIGHT:      We propose a meta-learning approach that "learns how to learn" models of altered dynamics within seconds of post-connection flight data.

48, TITLE:      DuReaderrobust: A Chinese Dataset Towards Evaluating the Robustness of Machine Reading Comprehension Models
http://arxiv.org/abs/2004.11142
AUTHORS:        Hongxuan Tang ; Jing Liu ; Hongyu Li ; Yu Hong ; Hua Wu ; Haifeng Wang
HIGHLIGHT:      It presents the robustness challenges when applying MRC models to real-world applications. To comprehensively evaluate the robustness of MRC models, we create a Chinese dataset, namely DuReader_{robust}.

49, TITLE:      gBeam-ACO: a greedy and faster variant of Beam-ACO
http://arxiv.org/abs/2004.11137
AUTHORS:        Jeff Hajewski ; Suely Oliveira ; David E. Stewart ; Laura Weiler
HIGHLIGHT:      In this work, we introduce a greedy variant of Beam-ACO that uses a greedy path selection heuristic.

50, 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 an provide an in-depth view how these architectures work.

51, TITLE:      Same Side Stance Classification Task: Facilitating Argument Stance Classification by Fine-tuning a BERT Model
http://arxiv.org/abs/2004.11163
AUTHORS:        Stefan Ollinger ; Lorik Dumani ; Premtim Sahitaj ; Ralph Bergmann ; Ralf Schenkel
HIGHLIGHT:      The results of our contribution to the task are build on a setup based on the BERT architecture.

52, TITLE:      On Adversarial Examples for Biomedical NLP Tasks
http://arxiv.org/abs/2004.11157
AUTHORS:        Vladimir Araujo ; Andres Carvallo ; Carlos Aspillaga ; Denis Parra
HIGHLIGHT:      For that reason, in this work, we propose an adversarial evaluation scheme on two well-known datasets for medical NER and STS.

53, TITLE:      Per-Step Reward: A New Perspective for Risk-Averse Reinforcement Learning
http://arxiv.org/abs/2004.10888
AUTHORS:        Shangtong Zhang ; Bo Liu ; Shimon Whiteson
HIGHLIGHT:      We present a new per-step reward perspective for risk-averse control in a discounted infinite horizon MDP.

54, TITLE:      Microscopy Image Restoration using Deep Learning on W2S
http://arxiv.org/abs/2004.10884
AUTHORS:        Martin Chatton
HIGHLIGHT:      We develop a deep learning algorithm based on the networks and method described in the recent W2S paper to solve a joint denoising and super-resolution problem.

55, TITLE:      Flexible and Efficient Long-Range Planning Through Curious Exploration
http://arxiv.org/abs/2004.10876
AUTHORS:        Aidan Curtis ; Minjian Xin ; Dilip Arumugam ; Kevin Feigelis ; Daniel Yamins
HIGHLIGHT:      Here, we propose the Curious Sample Planner (CSP), which fuses elements of TAMP and DRL by combining a curiosity-guided sampling strategy with imitation learning to accelerate planning.

56, TITLE:      Adaptive Operator Selection Based on Dynamic Thompson Sampling for MOEA/D
http://arxiv.org/abs/2004.10874
AUTHORS:        Lei Sun ; Ke Li
HIGHLIGHT:      This paper proposes a new AOS mechanism for multi-objective evolutionary algorithm based on decomposition (MOEA/D).

57, TITLE:      Some results on Vertex Separator Reconfiguration
http://arxiv.org/abs/2004.10873
AUTHORS:        Guilherme C. M. Gomes ; Sérgio H. Nogueira ; Vinicius F. dos Santos
COMMENTS:       21 pages, 8 figures
HIGHLIGHT:      We present the first results on the complexity of the reconfiguration of vertex separators under the three most popular rules: token addition/removal, token jumping, and token sliding.

58, TITLE:      What are We Depressed about When We Talk about COVID19: Mental Health Analysis on Tweets Using Natural Language Processing
http://arxiv.org/abs/2004.10899
AUTHORS:        Irene Li ; Yixin Li ; Tianxiao Li ; Sergio Alvarez-Napagao ; Dario Garcia
COMMENTS:       7 pages, 7 figures
HIGHLIGHT:      In this work, we focus on applying natural language processing (NLP) techniques to analyze tweets in terms of mental health. We build the EmoCT (Emotion-Covid19-Tweet) dataset for the training purpose by manually labeling 1,000 English tweets.

59, TITLE:      Improving the Decision-Making Process of Self-Adaptive Systems by Accounting for Tactic Volatility
http://arxiv.org/abs/2004.11302
AUTHORS:        Jeffrey Palmerino ; Qi Yu ; Travis Desell ; Daniel E. Krutz
HIGHLIGHT:      To address the challenge of sufficiently accounting for tactic volatility, we propose a Tactic Volatility Aware (TVA) solution.

60, TITLE:      CoInGP: Convolutional Inpainting with Genetic Programming
http://arxiv.org/abs/2004.11300
AUTHORS:        Domagoj Jakobovic ; Luca Manzoni ; Luca Mariot ; Stjepan Picek
COMMENTS:       14 pages, 6 figures
HIGHLIGHT:      We investigate the use of Genetic Programming (GP) as a convolutional predictor for supervised learning tasks in signal processing, focusing on the use case of predicting missing pixels in images.

61, TITLE:      Cooperative Perception with Deep Reinforcement Learning for Connected Vehicles
http://arxiv.org/abs/2004.10927
AUTHORS:        Shunsuke Aoki ; Takamasa Higuchi ; Onur Altintas
HIGHLIGHT:      In this paper, we present a cooperative perception scheme with deep reinforcement learning to enhance the detection accuracy for the surrounding objects.

62, TITLE:      PolyLaneNet: Lane Estimation via Deep Polynomial Regression
http://arxiv.org/abs/2004.10924
AUTHORS:        Lucas Tabelini ; Rodrigo Berriel ; Thiago M. Paixão ; Claudine Badue ; Alberto F. De Souza ; Thiago Oliveira-Santos
HIGHLIGHT:      In this work, we present a novel method for lane detection that uses as input an image from a forward-looking camera mounted in the vehicle and outputs polynomials representing each lane marking in the image, via deep polynomial regression.

63, TITLE:      TCNN: Triple Convolutional Neural Network Models for Retrieval-based Question Answering System in E-commerce
http://arxiv.org/abs/2004.10919
AUTHORS:        Shuangyong Song ; Chao Wang
COMMENTS:       2 pages
HIGHLIGHT:      In this paper, we aim to improve an IR based e-commerce QA system-AliMe with proposed text matching models, including a basic Triple Convolutional Neural Network (TCNN) model and two Attention-based TCNN (ATCNN) models.

64, TITLE:      Distilling Knowledge from Refinement in Multiple Instance Detection Networks
http://arxiv.org/abs/2004.10943
AUTHORS:        Luis Felipe Zeni ; Claudio Jung
COMMENTS:       published at CVPR 2020 Deepvision Workshop
HIGHLIGHT:      In this work, we claim that carefully selecting the aggregation criteria can considerably improve the accuracy of the learned detector.

65, TITLE:      YOLOv4: Optimal Speed and Accuracy of Object Detection
http://arxiv.org/abs/2004.10934
AUTHORS:        Alexey Bochkovskiy ; Chien-Yao Wang ; Hong-Yuan Mark Liao
HIGHLIGHT:      We assume that such universal features include Weighted-Residual-Connections (WRC), Cross-Stage-Partial-connections (CSP), Cross mini-Batch Normalization (CmBN), Self-adversarial-training (SAT) and Mish-activation.

66, TITLE:      PERMDNN: Efficient Compressed DNN Architecture with Permuted Diagonal Matrices
http://arxiv.org/abs/2004.10936
AUTHORS:        Chunhua Deng ; Siyu Liao ; Yi Xie ; Keshab K. Parhi ; Xuehai Qian ; Bo Yuan
HIGHLIGHT:      To address these drawbacks, this paper proposes PermDNN, a novel approach to generate and execute hardware-friendly structured sparse DNN models using permuted diagonal matrices.

67, TITLE:      Don't Stop Pretraining: Adapt Language Models to Domains and Tasks
http://arxiv.org/abs/2004.10964
AUTHORS:        Suchin Gururangan ; Ana Marasović ; Swabha Swayamdipta ; Kyle Lo ; Iz Beltagy ; Doug Downey ; Noah A. Smith
COMMENTS:       ACL 2020
HIGHLIGHT:      We present a study across four domains (biomedical and computer science publications, news, and reviews) and eight classification tasks, showing that a second phase of pretraining in-domain (domain-adaptive pretraining) leads to performance gains, under both high- and low-resource settings.

68, TITLE:      Metric-Learning-Assisted Domain Adaptation
http://arxiv.org/abs/2004.10963
AUTHORS:        Yueming Yin ; Zhen Yang ; Haifeng Hu ; Xiaofu Wu
HIGHLIGHT:      We thus propose a novel metric-learning-assisted domain adaptation (MLA-DA) method, which employs a novel triplet loss for helping better feature alignment.

69, TITLE:      Visual Question Answering Using Semantic Information from Image Descriptions
http://arxiv.org/abs/2004.10966
AUTHORS:        Tasmia Tasrin ; Md Sultan Al Nahian ; Brent Harrison
COMMENTS:       9 pages, 7 figures
HIGHLIGHT:      Considering these, we propose a deep neural network model that uses an attention mechanism which utilizes image features, the natural language question asked and semantic knowledge extracted from the image to produce open-ended answers for the given questions.

70, TITLE:      Joint Bilateral Learning for Real-time Universal Photorealistic Style Transfer
http://arxiv.org/abs/2004.10955
AUTHORS:        Xide Xia ; Meng Zhang ; Tianfan Xue ; Zheng Sun ; Hui Fang ; Brian Kulis ; Jiawen Chen
COMMENTS:       16 pages, 10 figures
HIGHLIGHT:      We propose a new end-to-end model for photorealistic style transfer that is both fast and inherently generates photorealistic results.

71, TITLE:      Few-Shot Class-Incremental Learning
http://arxiv.org/abs/2004.10956
AUTHORS:        Xiaoyu Tao ; Xiaopeng Hong ; Xinyuan Chang ; Songlin Dong ; Xing Wei ; Yihong Gong
HIGHLIGHT:      On this basis, we propose the TOpology-Preserving knowledge InCrementer (TOPIC) framework.

72, TITLE:      Uncertainty Quantification for Hyperspectral Image Denoising Frameworks based on Low-rank Matrix Approximation
http://arxiv.org/abs/2004.10959
AUTHORS:        Shaobo Xia ; Jingwei Song ; Dong Chen ; Jun Wang
COMMENTS:       7 Pages and 4 Figures
HIGHLIGHT:      To address this issue, we propose a prior-free closed-form element-wise uncertainty quantification method for the LRMA based HSI restoration.

73, TITLE:      Constructing Geographic and Long-term Temporal Graph for Traffic Forecasting
http://arxiv.org/abs/2004.10958
AUTHORS:        Yiwen Sun ; Yulu Wang ; Kun Fu ; Zheng Wang ; Changshui Zhang ; Jieping Ye
COMMENTS:       7 pages, 5 figures
HIGHLIGHT:      In this work, we propose Geographic and Long term Temporal Graph Convolutional Recurrent Neural Network (GLT-GCRNN), a novel framework for traffic forecasting that learns the rich interactions between roads sharing similar geographic or longterm temporal patterns.

74, TITLE:      COVID-19 Chest CT Image Segmentation -- A Deep Convolutional Neural Network Solution
http://arxiv.org/abs/2004.10987
AUTHORS:        Qingsen Yan ; Bo Wang ; Dong Gong ; Chuan Luo ; Wei Zhao ; Jianhu Shen ; Qinfeng Shi ; Shuo Jin ; Liang Zhang ; Zheng You
HIGHLIGHT:      In this work, we try to establish a new deep convolutional neural network tailored for segmenting the chest CT images with COVID-19 infections.

75, TITLE:      A Complete Characterization of Projectivity for Statistical Relational Models
http://arxiv.org/abs/2004.10984
AUTHORS:        Manfred Jaeger ; Oliver Schulte
COMMENTS:       To appear in Proceedings of IJCAI 2020
HIGHLIGHT:      In this paper we fill this gap: exploiting representation theorems for infinite exchangeable arrays we introduce a class of directed graphical latent variable models that precisely correspond to the class of projective relational models.

76, TITLE:      Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing
http://arxiv.org/abs/2004.10977
AUTHORS:        Hao Yan ; Marco Grasso ; Kamran Paynabar ; Bianca Maria Colosimo
HIGHLIGHT:      In this paper, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data.

77, TITLE:      Semi-Supervised Models via Data Augmentationfor Classifying Interactive Affective Responses
http://arxiv.org/abs/2004.10972
AUTHORS:        Jiaao Chen ; Yuwei Wu ; Diyi Yang
COMMENTS:       The AAAI-20 Workshop On Affective Content Analysis AFFCON2020: Interactive Affective Response
HIGHLIGHT:      We present semi-supervised models with data augmentation (SMDA), a semi-supervised text classification system to classify interactive affective responses.

78, TITLE:      Constructing Complexity-efficient Features in XCS with Tree-based Rule Conditions
http://arxiv.org/abs/2004.10978
AUTHORS:        Trung B. Nguyen ; Will N. Browne ; Mengjie Zhang
HIGHLIGHT:      This paper aims to optimise the structural efficiency of CFs in XOF.

79, TITLE:      BIT-VO: Visual Odometry at 300 FPS using Binary Features from the Focal Plane
http://arxiv.org/abs/2004.11186
AUTHORS:        Riku Murai ; Sajad Saeedi ; Paul H. J. Kelly
COMMENTS:       8 pages, 16 figures
HIGHLIGHT:      SCAMP-5 is a general-purpose FPSP used in this work and it carries out computations in the analog domain before analog to digital conversion.

80, TITLE:      Detection and Classification of Industrial Signal Lights for Factory Floors
http://arxiv.org/abs/2004.11187
AUTHORS:        Felix Nilsson ; Jens Jakobsen ; Fernando Alonso-Fernandez
HIGHLIGHT:      Accordingly, the goal is to develop a solution which can measure the operational state using the input from a video camera capturing a factory floor.

81, TITLE:      Depth-Wise Neural Architecture Search
http://arxiv.org/abs/2004.11178
AUTHORS:        Artur Jordao ; Fernando Akio ; Maiko Lie ; William Robson Schwartz
HIGHLIGHT:      Motivated by this, we propose a NAS approach to efficiently design accurate and low-cost convolutional architectures and demonstrate that an efficient strategy for designing these architectures is to learn the depth stage-by-stage.

82, TITLE:      Coloring Problems on Bipartite Graphs of Small Diameter
http://arxiv.org/abs/2004.11173
AUTHORS:        Victor A. Campos ; Guilherme C. M. Gomes ; Allen Ibiapina ; Raul Lopes ; Ignasi Sau ; Ana Silva
COMMENTS:       21 pages, 9 figures
HIGHLIGHT:      We investigate a number of coloring problems restricted to bipartite graphs with bounded diameter.

83, TITLE:      Cloud-Based Face and Speech Recognition for Access Control Applications
http://arxiv.org/abs/2004.11168
AUTHORS:        Nathalie Tkauc ; Thao Tran ; Kevin Hernandez-Diaz ; Fernando Alonso-Fernandez
HIGHLIGHT:      This paper describes the implementation of a system to recognize employees and visitors wanting to gain access to a physical office through face images and speech-to-text recognition.

84, TITLE:      Cpp-Taskflow: A General-purpose Parallel and Heterogeneous Task Programming System at Scale
http://arxiv.org/abs/2004.10908
AUTHORS:        Tsung-Wei Huang ; Dian-Lun Lin ; Yibo Lin ; Chun-Xun Lin
COMMENTS:       19 pages, 22 figures, submitted to SC20
HIGHLIGHT:      The Cpp-Taskflow project addresses the long-standing question: How can we make it easier for developers to write parallel and heterogeneous programs with high performance and simultaneous high productivity?

85, TITLE:      Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
http://arxiv.org/abs/2004.10904
AUTHORS:        Zhengqin Li ; Yu-Ying Yeh ; Manmohan Chandraker
COMMENTS:       Accepted by CVPR 2020 as an oral presentation
HIGHLIGHT:      Our novel contributions include a normal representation that enables the network to model complex light transport through local computation, a rendering layer that models refractions and reflections, a cost volume specifically designed for normal refinement of transparent shapes and a feature mapping based on predicted normals for 3D point cloud reconstruction.



==========Updates to Previous Papers==========
1, TITLE:       Reinforcement Learning in Healthcare: A Survey
http://arxiv.org/abs/1908.08796
AUTHORS:        Chao Yu ; Jiming Liu ; Shamim Nemati
HIGHLIGHT:      Such distinctive features make RL technique a suitable candidate for developing powerful solutions in a variety of healthcare domains, where diagnosing decisions or treatment regimes are usually characterized by a prolonged and sequential procedure.

2, TITLE:       Comparing Observation and Action Representations for Deep Reinforcement Learning in $μ$RTS
http://arxiv.org/abs/1910.12134
AUTHORS:        Shengyi Huang ; Santiago Ontañón
COMMENTS:       Presented in the AIIDE 2019 Workshop on Artificial Intelligence for Strategy Games
HIGHLIGHT:      This paper presents a preliminary study comparing different observation and action space representations for Deep Reinforcement Learning (DRL) in the context of Real-time Strategy (RTS) games.

3, TITLE:       ACNe: Attentive Context Normalization for Robust Permutation-Equivariant Learning
http://arxiv.org/abs/1907.02545
AUTHORS:        Weiwei Sun ; Wei Jiang ; Eduard Trulls ; Andrea Tagliasacchi ; Kwang Moo Yi
COMMENTS:       CVPR 2020
HIGHLIGHT:      In this paper, we propose Attentive Context Normalization (ACN), a simple yet effective technique to build permutation-equivariant networks robust to outliers.

4, TITLE:       Efficient Feature-based Image Registration by Mapping Sparsified Surfaces
http://arxiv.org/abs/1605.06215
AUTHORS:        Chun Pang Yung ; Gary P. T. Choi ; Ke Chen ; Lok Ming Lui
HIGHLIGHT:      Efficient Feature-based Image Registration by Mapping Sparsified Surfaces

5, TITLE:       VIFB: A Visible and Infrared Image Fusion Benchmark
http://arxiv.org/abs/2002.03322
AUTHORS:        Xingchen Zhang ; Ping Ye ; Gang Xiao
COMMENTS:       11 pages, 5 figures, 5 tables
HIGHLIGHT:      In this paper, after briefly reviewing recent advances of visible and infrared image fusion, we present a visible and infrared image fusion benchmark (VIFB) which consists of 21 image pairs, a code library of 20 fusion algorithms and 13 evaluation metrics.

6, TITLE:       Distinguish Confusing Law Articles for Legal Judgment Prediction
http://arxiv.org/abs/2004.02557
AUTHORS:        Nuo Xu ; Pinghui Wang ; Long Chen ; Li Pan ; Xiaoyan Wang ; Junzhou Zhao
COMMENTS:       This work has been accepted by the 58th Annual Meeting of the Association for Computational Linguistics (ACL 2020)
HIGHLIGHT:      In this paper, we present an end-to-end model, LADAN, to solve the task of LJP.

7, TITLE:       Attentive Modality Hopping Mechanism for Speech Emotion Recognition
http://arxiv.org/abs/1912.00846
AUTHORS:        Seunghyun Yoon ; Subhadeep Dey ; Hwanhee Lee ; Kyomin Jung
COMMENTS:       5 pages, Accepted as a conference paper at ICASSP 2020
HIGHLIGHT:      In this work, we explore the impact of visual modality in addition to speech and text for improving the accuracy of the emotion detection system.

8, TITLE:       FreeLB: Enhanced Adversarial Training for Natural Language Understanding
http://arxiv.org/abs/1909.11764
AUTHORS:        Chen Zhu ; Yu Cheng ; Zhe Gan ; Siqi Sun ; Tom Goldstein ; Jingjing Liu
COMMENTS:       Adding results with ALBERT
HIGHLIGHT:      In this work, we propose a novel adversarial training algorithm, FreeLB, that promotes higher invariance in the embedding space, by adding adversarial perturbations to word embeddings and minimizing the resultant adversarial risk inside different regions around input samples.

9, TITLE:       PhIT-Net: Photo-consistent Image Transform for Improved Matching
http://arxiv.org/abs/1911.12641
AUTHORS:        Damian Kaliroff ; Guy Gilboa
COMMENTS:       New and updated version of the article. The article describes the same algorithm as previous version. We updated and improved the writing of various sections of the article. New title and updated abstract, introduction, and related work sections. New metric for patch matching evaluation and comparison with additional methods. New visualizations of patch matching heatmaps
HIGHLIGHT:      We propose a new and completely data-driven approach for generating a photo-consistent image transform.

10, TITLE:      Combination of Multiple Global Descriptors for Image Retrieval
http://arxiv.org/abs/1903.10663
AUTHORS:        HeeJae Jun ; Byungsoo Ko ; Youngjoon Kim ; Insik Kim ; Jongtack Kim
HIGHLIGHT:      In this paper, we propose a novel framework that exploits multiple global descriptors to get an ensemble effect while it can be trained in an end-to-end manner.

11, TITLE:      KeypointNet: A Large-scale 3D Keypoint Dataset Aggregated from Numerous Human Annotations
http://arxiv.org/abs/2002.12687
AUTHORS:        Yang You ; Yujing Lou ; Chengkun Li ; Zhoujun Cheng ; Liangwei Li ; Lizhuang Ma ; Cewu Lu ; Weiming Wang
COMMENTS:       8 pages; to appear in CVPR 2020
HIGHLIGHT:      To handle the inconsistency between annotations from different people, we propose a novel method to aggregate these keypoints automatically, through minimization of a fidelity loss.

12, TITLE:      UNITER: UNiversal Image-TExt Representation Learning
http://arxiv.org/abs/1909.11740
AUTHORS:        Yen-Chun Chen ; Linjie Li ; Licheng Yu ; Ahmed El Kholy ; Faisal Ahmed ; Zhe Gan ; Yu Cheng ; Jingjing Liu
HIGHLIGHT:      In this paper, we introduce UNITER, a UNiversal Image-TExt Representation, learned through large-scale pre-training over four image-text datasets (COCO, Visual Genome, Conceptual Captions, and SBU Captions), which can power heterogeneous downstream V+L tasks with joint multimodal embeddings.

13, TITLE:      Multi-Agent Task-Oriented Dialog Policy Learning with Role-Aware Reward Decomposition
http://arxiv.org/abs/2004.03809
AUTHORS:        Ryuichi Takanobu ; Runze Liang ; Minlie Huang
COMMENTS:       ACL 2020 long paper
HIGHLIGHT:      To avoid explicitly building a user simulator beforehand, we propose Multi-Agent Dialog Policy Learning, which regards both the system and the user as the dialog agents.

14, TITLE:      Embedding Expansion: Augmentation in Embedding Space for Deep Metric Learning
http://arxiv.org/abs/2003.02546
AUTHORS:        Byungsoo Ko ; Geonmo Gu
COMMENTS:       Accepted by CVPR 2020
HIGHLIGHT:      In this paper, inspired by query expansion and database augmentation, we propose an augmentation method in an embedding space for pair-based metric learning losses, called embedding expansion.

15, TITLE:      Analyzing the Forgetting Problem in the Pretrain-Finetuning of Dialogue Response Models
http://arxiv.org/abs/1910.07117
AUTHORS:        Tianxing He ; Jun Liu ; Kyunghyun Cho ; Myle Ott ; Bing Liu ; James Glass ; Fuchun Peng
HIGHLIGHT:      In this work, we study how the large-scale pretrain-finetune framework changes the behavior of a neural language generator.

16, TITLE:      A bisector line field approach to interpolation of orientation fields
http://arxiv.org/abs/1907.11449
AUTHORS:        Nicolas Boizot ; Ludovic Sacchelli
HIGHLIGHT:      We propose an approach to the problem of global reconstruction of an orientation field.

17, TITLE:      Instance Segmentation of Biological Images Using Harmonic Embeddings
http://arxiv.org/abs/1904.05257
AUTHORS:        Victor Kulikov ; Victor Lempitsky
COMMENTS:       Accepted as oral to CVPR 2020
HIGHLIGHT:      We present a new instance segmentation approach tailored to biological images, where instances may correspond to individual cells, organisms or plant parts.

18, TITLE:      An Empirical Study on Position of the Batch Normalization Layer in Convolutional Neural Networks
http://arxiv.org/abs/1912.04259
AUTHORS:        Moein Hasani ; Hassan Khotanlou
HIGHLIGHT:      In this paper, we have studied how the training of the convolutional neural networks (CNNs) can be affected by changing the position of the batch normalization (BN) layer.

19, TITLE:      Understanding Image Captioning Models beyond Visualizing Attention
http://arxiv.org/abs/2001.01037
AUTHORS:        Jiamei Sun ; Sebastian Lapuschkin ; Wojciech Samek ; Alexander Binder
HIGHLIGHT:      In this paper, we develop variants of layer-wise relevance backpropagation (LRP) and gradient backpropagation, tailored to image captioning models with attention mechanisms.

20, TITLE:      AD-Cluster: Augmented Discriminative Clustering for Domain Adaptive Person Re-identification
http://arxiv.org/abs/2004.08787
AUTHORS:        Yunpeng Zhai ; Shijian Lu ; Qixiang Ye ; Xuebo Shan ; Jie Chen ; Rongrong Ji ; Yonghong Tian
COMMENTS:       Accepted by CVPR'20
HIGHLIGHT:      This paper presents a novel augmented discriminative clustering (AD-Cluster) technique that estimates and augments person clusters in target domains and enforces the discrimination ability of re-ID models with the augmented clusters.

21, TITLE:      Self-Constructing Graph Convolutional Networks for Semantic Labeling
http://arxiv.org/abs/2003.06932
AUTHORS:        Qinghui Liu ; Michael Kampffmeyer ; Robert Jenssen ; Arnt-Børre Salberg
COMMENTS:       IGARSS-2020, code at: github.com/samleoqh/MSCG-Net
HIGHLIGHT:      Here, we propose a novel architecture called the Self-Constructing Graph (SCG), which makes use of learnable latent variables to generate embeddings and to self-construct the underlying graphs directly from the input features without relying on manually built prior knowledge graphs.

22, TITLE:      Harnessing the linguistic signal to predict scalar inferences
http://arxiv.org/abs/1910.14254
AUTHORS:        Sebastian Schuster ; Yuxing Chen ; Judith Degen
COMMENTS:       ACL 2020; 16 pages, 8 figures
HIGHLIGHT:      In this work, we explore to what extent neural network sentence encoders can learn to predict the strength of scalar inferences.

23, TITLE:      Toward A Neuro-inspired Creative Decoder
http://arxiv.org/abs/1902.02399
AUTHORS:        Payel Das ; Brian Quanz ; Pin-Yu Chen ; Jae-wook Ahn ; Dhruv Shah
COMMENTS:       Accepted to IJCAI 2020
HIGHLIGHT:      Inspired by this seminal finding, in this study we propose a creative decoder within a deep generative framework, which involves direct modulation of the neuronal activation pattern after sampling from the learned latent space.

24, TITLE:      Illumination-Adaptive Person Re-identification
http://arxiv.org/abs/1905.04525
AUTHORS:        Zelong Zeng ; Zhixiang Wang ; Zheng Wang ; Yinqiang Zheng ; Yung-Yu Chuang ; Shin'ichi Satoh
COMMENTS:       Accepted by TMM
HIGHLIGHT:      We propose an Illumination-Identity Disentanglement (IID) network to dispel different scales of illuminations away while preserving individuals' identity information. To demonstrate the illumination issue and to evaluate our model, we construct two large-scale simulated datasets with a wide range of illumination variations.

25, TITLE:      Large Arabic Twitter Dataset on COVID-19
http://arxiv.org/abs/2004.04315
AUTHORS:        Sarah Alqurashi ; Ahmad Alhindi ; Eisa Alanazi
HIGHLIGHT:      In this work, we describe the first Arabic tweets dataset on COVID-19 that we have been collecting since January 1st, 2020.

26, TITLE:      Low-Resolution Overhead Thermal Tripwire for Occupancy Estimation
http://arxiv.org/abs/2004.05685
AUTHORS:        Mertcan Cokbas ; Prakash Ishwar ; Janusz Konrad
HIGHLIGHT:      We propose a people counting system which uses a low-resolution thermal sensor. To evaluate our algorithms, we have collected and labeled a low-resolution thermal video dataset using the proposed system.

27, TITLE:      Learning to solve the credit assignment problem
http://arxiv.org/abs/1906.00889
AUTHORS:        Benjamin James Lansdell ; Prashanth Ravi Prakash ; Konrad Paul Kording
COMMENTS:       18 pages; 4 figures. (ICLR 2020 version)
HIGHLIGHT:      Here we propose a hybrid learning approach.

28, TITLE:      Rethinking Data Augmentation for Image Super-resolution: A Comprehensive Analysis and a New Strategy
http://arxiv.org/abs/2004.00448
AUTHORS:        Jaejun Yoo ; Namhyuk Ahn ; Kyung-Ah Sohn
HIGHLIGHT:      In this paper, we provide a comprehensive analysis of the existing augmentation methods applied to the super-resolution task.

29, TITLE:      Fingerprint Synthesis: Search with 100 Million Prints
http://arxiv.org/abs/1912.07195
AUTHORS:        Vishesh Mistry ; Joshua J. Engelsma ; Anil K. Jain
HIGHLIGHT:      To address this problem, we utilize a Generative Adversarial Network (GAN) to synthesize a fingerprint dataset consisting of 100 million fingerprint images.

30, TITLE:      Symmetrical Synthesis for Deep Metric Learning
http://arxiv.org/abs/2001.11658
AUTHORS:        Geonmo Gu ; Byungsoo Ko
COMMENTS:       Accepted by AAAI 2020
HIGHLIGHT:      In this paper, we address these problems by proposing a novel method of synthetic hard sample generation called symmetrical synthesis.

31, TITLE:      Polytopes, lattices, and spherical codes for the nearest neighbor problem
http://arxiv.org/abs/1907.04628
AUTHORS:        Thijs Laarhoven
COMMENTS:       This is the full version of the paper published in the proceedings of ICALP 2020 under the same title, which only contains Section 1
HIGHLIGHT:      We study locality-sensitive hash methods for the nearest neighbor problem for the angular distance, focusing on the approach of first projecting down onto a low-dimensional subspace, and then partitioning the projected vectors according to Voronoi cells induced by a suitable spherical code.

32, TITLE:      Field-based Coordination with the Share Operator
http://arxiv.org/abs/1910.02874
AUTHORS:        Giorgio Audrito ; Jacob Beal ; Ferruccio Damiani ; Danilo Pianini ; Mirko Viroli
HIGHLIGHT:      In this paper, we propose a new field-based coordination operator called share, which captures the space-time nature of field computations in a single operator that declaratively achieves: (i) observation of neighbors' values; (ii) reduction to a single local value; and iii) update and converse sharing to neighbors of a local variable.

33, TITLE:      Computational Complexity of the Hylland-Zeckhauser Scheme for One-Sided Matching Markets
http://arxiv.org/abs/2004.01348
AUTHORS:        Vijay V. Vazirani ; Mihalis Yannakakis
COMMENTS:       22 pages
HIGHLIGHT:      We present the following partial resolution: 1.

34, TITLE:      Location-Relative Attention Mechanisms For Robust Long-Form Speech Synthesis
http://arxiv.org/abs/1910.10288
AUTHORS:        Eric Battenberg ; RJ Skerry-Ryan ; Soroosh Mariooryad ; Daisy Stanton ; David Kao ; Matt Shannon ; Tom Bagby
COMMENTS:       Accepted to ICASSP 2020
HIGHLIGHT:      We suggest simple modifications to GMM-based attention that allow it to align quickly and consistently during training, and introduce a new location-relative attention mechanism to the additive energy-based family, called Dynamic Convolution Attention (DCA).

35, TITLE:      tax2vec: Constructing Interpretable Features from Taxonomies for Short Text Classification
http://arxiv.org/abs/1902.00438
AUTHORS:        Blaž Škrlj ; Matej Martinc ; Jan Kralj ; Nada Lavrač ; Senja Pollak
COMMENTS:       Accepted at CSL journal
HIGHLIGHT:      We propose tax2vec, a parallel algorithm for constructing taxonomy-based features, and demonstrate its use on six short text classification problems: prediction of gender, personality type, age, news topics, drug side effects and drug effectiveness.

36, TITLE:      Cascaded Context Enhancement for Automated Skin Lesion Segmentation
http://arxiv.org/abs/2004.08107
AUTHORS:        Ruxin Wang ; Shuyuan Chen ; Jianping Fan ; Ye Li
HIGHLIGHT:      In this paper, we formulate a cascaded context enhancement neural network for skin lesion segmentation.

37, TITLE:      Stanza: A Python Natural Language Processing Toolkit for Many Human Languages
http://arxiv.org/abs/2003.07082
AUTHORS:        Peng Qi ; Yuhao Zhang ; Yuhui Zhang ; Jason Bolton ; Christopher D. Manning
COMMENTS:       ACL2020 System Demonstration. First two authors contribute equally. Website: https://stanfordnlp.github.io/stanza
HIGHLIGHT:      We introduce Stanza, an open-source Python natural language processing toolkit supporting 66 human languages.

38, TITLE:      Mapping the Landscape of Artificial Intelligence Applications against COVID-19
http://arxiv.org/abs/2003.11336
AUTHORS:        Joseph Bullock ; Alexandra Luccioni ; Katherine Hoffmann Pham ; Cynthia Sin Nga Lam ; Miguel Luengo-Oroz
COMMENTS:       32 pages, v2: much larger to reflect the significant increase in the size of the body of literature
HIGHLIGHT:      In this review, we present an overview of recent studies using Machine Learning and, more broadly, Artificial Intelligence, to tackle many aspects of the COVID-19 crisis at different scales including molecular, clinical, and societal applications.

39, TITLE:      Norm-Preservation: Why Residual Networks Can Become Extremely Deep?
http://arxiv.org/abs/1805.07477
AUTHORS:        Alireza Zaeemzadeh ; Nazanin Rahnavard ; Mubarak Shah
HIGHLIGHT:      We answer this question by proposing an efficient method to regularize the singular values of the convolution operator and making the ResNet's transition layers extra norm-preserving.

40, TITLE:      PoKi: A Large Dataset of Poems by Children
http://arxiv.org/abs/2004.06188
AUTHORS:        Will E. Hipson ; Saif M. Mohammad
HIGHLIGHT:      We present a new corpus of child-written text, PoKi, which includes about 62 thousand poems written by children from grades 1 to 12.

41, TITLE:      Computations with Greater Quantum Depth Are Strictly More Powerful (Relative to an Oracle)
http://arxiv.org/abs/1909.10503
AUTHORS:        Matthew Coudron ; Sanketh Menda
COMMENTS:       39 pages, revised
HIGHLIGHT:      In this work we show that the Welded Tree Problem, which is an oracle problem that can be solved in quantum polynomial time as shown by Childs et al. (arXiv:quant-ph/0209131), cannot be solved in $\textrm{BPP}^{\textrm{BQNC}}$, nor can it be solved in the class that Jozsa describes.

42, TITLE:      A Benchmark on Tricks for Large-scale Image Retrieval
http://arxiv.org/abs/1907.11854
AUTHORS:        Byungsoo Ko ; Minchul Shin ; Geonmo Gu ; HeeJae Jun ; Tae Kwan Lee ; Youngjoon Kim
HIGHLIGHT:      In this paper, we extensively analyze the effect of well-known pre-processing, post-processing tricks, and their combination for large-scale image retrieval.

43, TITLE:      Wasserstein GANs for MR Imaging: from Paired to Unpaired Training
http://arxiv.org/abs/1910.07048
AUTHORS:        Ke Lei ; Morteza Mardani ; John M. Pauly ; Shreyas S. Vasanawala
HIGHLIGHT:      To cope with this challenge, this paper leverages unpaired adversarial training for reconstruction networks, where the inputs are undersampled k-space and naively reconstructed images from one dataset, and the labels are high-quality images from another dataset.

44, TITLE:      AutoEG: Automated Experience Grafting for Off-Policy Deep Reinforcement Learning
http://arxiv.org/abs/2004.10698
AUTHORS:        Keting Lu ; Shiqi Zhang ; Xiaoping Chen
HIGHLIGHT:      Focusing on addressing this limitation, this paper makes a twofold contribution.

45, TITLE:      Aerial Images Processing for Car Detection using Convolutional Neural Networks: Comparison between Faster R-CNN and YoloV3
http://arxiv.org/abs/1910.07234
AUTHORS:        Adel Ammar ; Anis Koubaa ; Mohanned Ahmed ; Abdulrahman Saad
HIGHLIGHT:      In this paper, we address the problem of car detection from aerial images using Convolutional Neural Networks (CNN).

46, TITLE:      Combined Model for Partially-Observable and Non-Observable Task Switching: Solving Hierarchical Reinforcement Learning Problems Statically and Dynamically with Transfer Learning
http://arxiv.org/abs/2004.06213
AUTHORS:        Nibraas Khan ; Joshua Phillips
COMMENTS:       substantial text overlap with arXiv:1911.10425 which is the same as this paper. It was meant to be a revision of that paper, but I mistakenly submitted it as a new paper
HIGHLIGHT:      We propose a new model, PONOWMtk, which combines both approaches, ATRs and input storage, with a static or dynamic number of ATRs.

47, TITLE:      On the Language Neutrality of Pre-trained Multilingual Representations
http://arxiv.org/abs/2004.05160
AUTHORS:        Jindřich Libovický ; Rudolf Rosa ; Alexander Fraser
COMMENTS:       11 pages, 3 figures. arXiv admin note: text overlap with arXiv:1911.03310
HIGHLIGHT:      We instead focus on the language-neutrality of mBERT with respect to lexical semantics.

48, TITLE:      Deep Learning for Screening COVID-19 using Chest X-Ray Images
http://arxiv.org/abs/2004.10507
AUTHORS:        Sanhita Basu ; Sushmita Mitra
HIGHLIGHT:      Therefore, we propose a new concept called domain extension transfer learning (DETL).

49, TITLE:      Reasoning about Typicality and Probabilities in Preferential Description Logics
http://arxiv.org/abs/2004.09507
AUTHORS:        Laura Giordano ; Valentina Gliozzi ; Antonio Lieto ; Nicola Olivetti ; Gian Luca Pozzato
COMMENTS:       17 pages. arXiv admin note: text overlap with arXiv:1811.02366
HIGHLIGHT:      In this work we describe preferential Description Logics of typicality, a nonmonotonic extension of standard Description Logics by means of a typicality operator T allowing to extend a knowledge base with inclusions of the form T(C) v D, whose intuitive meaning is that normally/typically Cs are also Ds.

50, TITLE:      The 1st Agriculture-Vision Challenge: Methods and Results
http://arxiv.org/abs/2004.09754
AUTHORS:        Mang Tik Chiu ; Xingqian Xu ; Kai Wang ; Jennifer Hobbs ; Naira Hovakimyan ; Thomas S. Huang ; Honghui Shi ; Yunchao Wei ; Zilong Huang ; Alexander Schwing ; Robert Brunner ; Ivan Dozier ; Wyatt Dozier ; Karen Ghandilyan ; David Wilson ; Hyunseong Park ; Junhee Kim ; Sungho Kim ; Qinghui Liu ; Michael C. Kampffmeyer ; Robert Jenssen ; Arnt B. Salberg ; Alexandre Barbosa ; Rodrigo Trevisan ; Bingchen Zhao ; Shaozuo Yu ; Siwei Yang ; Yin Wang ; Hao Sheng ; Xiao Chen ; Jingyi Su ; Ram Rajagopal ; Andrew Ng ; Van Thong Huynh ; Soo-Hyung Kim ; In-Seop Na ; Ujjwal Baid ; Shubham Innani ; Prasad Dutande ; Bhakti Baheti ; Sanjay Talbar ; Jianyu Tang
COMMENTS:       CVPR 2020 Workshop
HIGHLIGHT:      The first Agriculture-Vision Challenge aims to encourage research in developing novel and effective algorithms for agricultural pattern recognition from aerial images, especially for the semantic segmentation task associated with our challenge dataset.

51, TITLE:      Improving Transformer Models by Reordering their Sublayers
http://arxiv.org/abs/1911.03864
AUTHORS:        Ofir Press ; Noah A. Smith ; Omer Levy
COMMENTS:       To appear at ACL 2020
HIGHLIGHT:      We propose a new transformer pattern that adheres to this property, the sandwich transformer, and show that it improves perplexity on multiple word-level and character-level language modeling benchmarks, at no cost in parameters, memory, or training time.

52, TITLE:      Game on Random Environment, Mean-field Langevin System and Neural Networks
http://arxiv.org/abs/2004.02457
AUTHORS:        Giovanni Conforti ; Anna Kazeykina ; Zhenjie Ren
HIGHLIGHT:      In this paper we study a type of games regularized by the relative entropy, where the players' strategies are coupled through a random environment variable.

53, TITLE:      The Devil is in the Channels: Mutual-Channel Loss for Fine-Grained Image Classification
http://arxiv.org/abs/2002.04264
AUTHORS:        Dongliang Chang ; Yifeng Ding ; Jiyang Xie ; Ayan Kumar Bhunia ; Xiaoxu Li ; Zhanyu Ma ; Ming Wu ; Jun Guo ; Yi-Zhe Song
COMMENTS:       TIP2020. Code available at https://github.com/dongliangchang/Mutual-Channel-Loss
HIGHLIGHT:      In this paper, we show it is possible to cultivate subtle details without the need for overly complicated network designs or training mechanisms -- a single loss is all it takes.

54, TITLE:      End-to-End Abstractive Summarization for Meetings
http://arxiv.org/abs/2004.02016
AUTHORS:        Chenguang Zhu ; Ruochen Xu ; Michael Zeng ; Xuedong Huang
COMMENTS:       12 pages, 2 figures
HIGHLIGHT:      In this paper, we propose a novel end-to-end abstractive summary network that adapts to the meeting scenario.

55, TITLE:      How To Train Your Deep Multi-Object Tracker
http://arxiv.org/abs/1906.06618
AUTHORS:        Yihong Xu ; Aljosa Osep ; Yutong Ban ; Radu Horaud ; Laura Leal-Taixe ; Xavier Alameda-Pineda
COMMENTS:       14 pages, 9 figures, 6 tables
HIGHLIGHT:      In this paper, we bridge this gap by proposing a differentiable proxy of MOTA and MOTP, which we combine in a loss function suitable for end-to-end training of deep multi-object trackers.