2020.05.06.txt

==========New Papers==========
1, TITLE:       Reproduction of Lateral Inhibition-Inspired Convolutional Neural Network for Visual Attention and Saliency Detection
http://arxiv.org/abs/2005.02184
AUTHORS:        Filip Marcinek
HIGHLIGHT:      In my work, I analyze the above problem using for this purpose saliency maps created by the LICNN network.

2, TITLE:       A multi-component framework for the analysis and design of explainable artificial intelligence
http://arxiv.org/abs/2005.01908
AUTHORS:        S. Atakishiyev ; H. Babiker ; N. Farruque ; R. Goebel1 ; M-Y. Kima ; M. H. Motallebi ; J. Rabelo ; T. Syed ; O. R. Zaïane
COMMENTS:       39 pages
HIGHLIGHT:      Here we intend to provide a strategic inventory of XAI requirements, demonstrate their connection to a history of XAI ideas, and synthesize those ideas into a simple framework to calibrate five successive levels of XAI.

3, TITLE:       3D Tomographic Pattern Synthesis for Enhancing the Quantification of COVID-19
http://arxiv.org/abs/2005.01903
AUTHORS:        Siqi Liu ; Bogdan Georgescu ; Zhoubing Xu ; Youngjin Yoo ; Guillaume Chabin ; Shikha Chaganti ; Sasa Grbic ; Sebastian Piat ; Brian Teixeira ; Abishek Balachandran ; Vishwanath RS ; Thomas Re ; Dorin Comaniciu
HIGHLIGHT:      In this paper, we propose to use synthetic datasets to augment an existing COVID-19 database to tackle these challenges.

4, TITLE:       OpinionDigest: A Simple Framework for Opinion Summarization
http://arxiv.org/abs/2005.01901
AUTHORS:        Yoshihiko Suhara ; Xiaolan Wang ; Stefanos Angelidis ; Wang-Chiew Tan
COMMENTS:       ACL 2020 (short paper)
HIGHLIGHT:      We present OpinionDigest, an abstractive opinion summarization framework, which does not rely on gold-standard summaries for training.

5, TITLE:       Modal features for image texture classification
http://arxiv.org/abs/2005.01928
AUTHORS:        Thomas Lacombe ; Hugues Favreliere ; Maurice Pillet
COMMENTS:       12 pages, 5 figures. Accepted and to be published in Pattern Recognition Letters
HIGHLIGHT:      In this article, a new feature extraction method based on Discrete Modal Decomposition (DMD) is introduced, to extend the group of space and frequency based features.

6, TITLE:       StereoGAN: Bridging Synthetic-to-Real Domain Gap by Joint Optimization of Domain Translation and Stereo Matching
http://arxiv.org/abs/2005.01927
AUTHORS:        Rui Liu ; Chengxi Yang ; Wenxiu Sun ; Xiaogang Wang ; Hongsheng Li
COMMENTS:       Accepted to CVPR2020
HIGHLIGHT:      In this paper, we propose an end-to-end training framework with domain translation and stereo matching networks to tackle this challenge.

7, TITLE:       Generating Thermal Image Data Samples using 3D Facial Modelling Techniques and Deep Learning Methodologies
http://arxiv.org/abs/2005.01923
AUTHORS:        Muhammad Ali Farooq ; Peter Corcoran
COMMENTS:       Paper accpeted in QOMEX IEEE 2020 Conference
HIGHLIGHT:      In this work, we extend existing methodologies to show how 2D thermal facial data can be mapped to provide 3D facial models.

8, TITLE:       From Image Collections to Point Clouds with Self-supervised Shape and Pose Networks
http://arxiv.org/abs/2005.01939
AUTHORS:        K L Navaneet ; Ansu Mathew ; Shashank Kashyap ; Wei-Chih Hung ; Varun Jampani ; R. Venkatesh Babu
COMMENTS:       Accepted to CVPR 2020; Codes are available at https://github.com/val-iisc/ssl_3d_recon
HIGHLIGHT:      In this work, we propose a deep learning technique for 3D object reconstruction from a single image.

9, TITLE:       Probabilistic End-to-End Vehicle Navigation in Complex Dynamic Environments with Multimodal Sensor Fusion
http://arxiv.org/abs/2005.01935
AUTHORS:        Peide Cai ; Sukai Wang ; Yuxiang Sun ; Ming Liu
COMMENTS:       8 pages, 6 figures, 3 tables. IEEE Robotics and Automation Letters (RA-L)
HIGHLIGHT:      In this paper, based on imitation learning, we propose a probabilistic driving model with ultiperception capability utilizing the information from the camera, lidar and radar.

10, TITLE:      ExpBERT: Representation Engineering with Natural Language Explanations
http://arxiv.org/abs/2005.01932
AUTHORS:        Shikhar Murty ; Pang Wei Koh ; Percy Liang
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we allow model developers to specify these types of inductive biases as natural language explanations.

11, TITLE:      Small, Sparse, but Substantial: Techniques for Segmenting Small Agricultural Fields Using Sparse Ground Data
http://arxiv.org/abs/2005.01947
AUTHORS:        Smit Marvaniya ; Umamaheswari Devi ; Jagabondhu Hazra ; Shashank Mujumdar ; Nitin Gupta
COMMENTS:       7 pages, 6 figures
HIGHLIGHT:      Hence, in this paper, we present a multi-stage approach that uses a combination of machine learning and image processing techniques.

12, TITLE:      AlignShift: Bridging the Gap of Imaging Thickness in 3D Anisotropic Volumes
http://arxiv.org/abs/2005.01969
AUTHORS:        Jiancheng Yang ; Yi He ; Xiaoyang Huang ; Jingwei Xu ; Xiaodan Ye ; Guangyu Tao ; Bingbing Ni
COMMENTS:       Preprint. Code is available at https://github.com/M3DV/AlignShift
HIGHLIGHT:      We aim at a unified approach for both thin- and thick-slice medical volumes.

13, TITLE:      Characterizing Triviality of the Exponent Lattice of A Polynomial through Galois and Galois-Like Groups
http://arxiv.org/abs/2005.01963
AUTHORS:        Tao Zheng
COMMENTS:       19 pages,2 figures
HIGHLIGHT:      In this paper, the relations between the Galois group (respectively, \emph{the Galois-like groups}) and the triviality of the exponent lattice of a polynomial are investigated.

14, TITLE:      Reconciling progress-insensitive noninterference and declassification
http://arxiv.org/abs/2005.01977
AUTHORS:        Johan Bay ; Aslan Askarov
HIGHLIGHT:      We believe that the connection established in this work will enable other applications of ideas from the literature on declassification to progress insensitivity.

15, TITLE:      End-to-end Whispered Speech Recognition with Frequency-weighted Approaches and Layer-wise Transfer Learning
http://arxiv.org/abs/2005.01972
AUTHORS:        Heng-Jui Chang ; Alexander H. Liu ; Hung-yi Lee ; Lin-shan Lee
COMMENTS:       submitted to INTERSPEECH 2020
HIGHLIGHT:      In this paper, we present several approaches for end-to-end (E2E) recognition of whispered speech considering the special characteristics of whispered speech and the scarcity of data.

16, TITLE:      Advice for Online Knapsack With Removable Items
http://arxiv.org/abs/2005.01867
AUTHORS:        Hans-Joachim Böckenhauer ; Jan Dreier ; Fabian Frei ; Peter Rossmanith
HIGHLIGHT:      We analyze the advice complexity of this problem.

17, TITLE:      Soft Gazetteers for Low-Resource Named Entity Recognition
http://arxiv.org/abs/2005.01866
AUTHORS:        Shruti Rijhwani ; Shuyan Zhou ; Graham Neubig ; Jaime Carbonell
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking.

18, TITLE:      Streaming Object Detection for 3-D Point Clouds
http://arxiv.org/abs/2005.01864
AUTHORS:        Wei Han ; Zhengdong Zhang ; Benjamin Caine ; Brandon Yang ; Christoph Sprunk ; Ouais Alsharif ; Jiquan Ngiam ; Vijay Vasudevan ; Jonathon Shlens ; Zhifeng Chen
HIGHLIGHT:      In this work, we explore how to build an object detector that removes this artificial latency constraint, and instead operates on native streaming data in order to significantly reduce latency.

19, TITLE:      Complex Amplitude-Phase Boltzmann Machines
http://arxiv.org/abs/2005.01862
AUTHORS:        Zengyi Li ; Friedrich T. Sommer
COMMENTS:       Short Technical Note
HIGHLIGHT:      We extend the framework of Boltzmann machines to a network of complex-valued neurons with variable amplitudes, referred to as Complex Amplitude-Phase Boltzmann machine (CAP-BM).

20, TITLE:      Illumination-Invariant Image from 4-Channel Images: The Effect of Near-Infrared Data in Shadow Removal
http://arxiv.org/abs/2005.01878
AUTHORS:        Sorour Mohajerani ; Mark S. Drew ; Parvaneh Saeedi
COMMENTS:       Accepted for oral presentation in London Imaging Meeting 2020
HIGHLIGHT:      In this study, we examine the quality of illumination-invariant images generated from red, green, blue, and near-infrared (RGBN) data.

21, TITLE:      FarsBase-KBP: A Knowledge Base Population System for the Persian Knowledge Graph
http://arxiv.org/abs/2005.01879
AUTHORS:        Majid Asgari-Bidhendi ; Behrooz Janfada ; Behrouz Minaei-Bidgoli
COMMENTS:       39 pages, 6 figures
HIGHLIGHT:      In this paper, we present a knowledge base population system for the Persian language, which extracts knowledge from unlabeled raw text, crawled from the Web. To evaluate the performance of the presented knowledge base population system, we present the first gold dataset for benchmarking knowledge base population in the Persian language, which consisting of 22015 FarsBase triples and verified by human experts.

22, TITLE:      Probabilistic Assumptions Matter: Improved Models for Distantly-Supervised Document-Level Question Answering
http://arxiv.org/abs/2005.01898
AUTHORS:        Hao Cheng ; Ming-Wei Chang ; Kenton Lee ; Kristina Toutanova
COMMENTS:       ACL2020
HIGHLIGHT:      We address the problem of extractive question answering using document-level distant super-vision, pairing questions and relevant documents with answer strings.

23, TITLE:      Fine-grained Opinion Mining in Financial Data: A Survey and Research Agenda
http://arxiv.org/abs/2005.01897
AUTHORS:        Chung-Chi Chen ; Hen-Hsen Huang ; Hsin-Hsi Chen
HIGHLIGHT:      Fine-grained Opinion Mining in Financial Data: A Survey and Research Agenda

24, TITLE:      Multistage Committee Election
http://arxiv.org/abs/2005.02300
AUTHORS:        Robert Bredereck ; Till Fluschnik ; Andrzej Kaczmarczyk
HIGHLIGHT:      Being interested in a sequence of committees, we introduce and study two time-dependent multistage models based on simple Plurality voting.

25, TITLE:      Generalized Planning With Deep Reinforcement Learning
http://arxiv.org/abs/2005.02305
AUTHORS:        Or Rivlin ; Tamir Hazan ; Erez Karpas
COMMENTS:       13 pages
HIGHLIGHT:      In this work we study the use of Deep Reinforcement Learning and Graph Neural Networks to learn such generalized policies and demonstrate that they can generalize to instances that are orders of magnitude larger than those they were trained on.

26, TITLE:      Multi-interactive Encoder-decoder Network for RGBT Salient Object Detection
http://arxiv.org/abs/2005.02315
AUTHORS:        Zhengzheng Tu ; Zhun Li ; Chenglong Li ; Yang Lang ; Jin Tang
COMMENTS:       14 pages, 6 figures
HIGHLIGHT:      In this paper, we propose a novel multi-interactive encoder-decoder network to achieve an elaborative fusion for RGBT SOD.

27, TITLE:      Adversarial Training against Location-Optimized Adversarial Patches
http://arxiv.org/abs/2005.02313
AUTHORS:        Sukrut Rao ; David Stutz ; Bernt Schiele
COMMENTS:       18 pages, 6 tables, 4 figures, 2 algorithms
HIGHLIGHT:      Adversarial Training against Location-Optimized Adversarial Patches

28, TITLE:      Digraph of Senegal s local languages: issues, challenges and prospects of their transliteration
http://arxiv.org/abs/2005.02325
AUTHORS:        Elhadji Mamadou Nguer ; Diop Sokhna Bao ; Yacoub Ahmed Fall ; Mouhamadou Khoule
HIGHLIGHT:      Our goal will consist, on the one hand in raising the issues related to the transliteration and the challenges that this will raise, and on the other one, presenting the perspectives.

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

30, TITLE:      Don't Explain without Verifying Veracity: An Evaluation of Explainable AI with Video Activity Recognition
http://arxiv.org/abs/2005.02335
AUTHORS:        Mahsan Nourani ; Chiradeep Roy ; Tahrima Rahman ; Eric D. Ragan ; Nicholas Ruozzi ; Vibhav Gogate
HIGHLIGHT:      In this paper, we explore how explanation veracity affects user performance and agreement in intelligent systems.

31, TITLE:      Heuristic-Based Weak Learning for Moral Decision-Making
http://arxiv.org/abs/2005.02342
AUTHORS:        Ryan Steed
COMMENTS:       39 pages, 14 figures. Undergraduate thesis
HIGHLIGHT:      To lower the barrier to training moral agents, I develop a heuristic-based weak learning approach to moral decision-making.

32, TITLE:      Effect of the sEMG electrode (re)placement and feature set size on the hand movement recognition
http://arxiv.org/abs/2005.02105
AUTHORS:        Nadica Miljković ; Milica S. Isaković
COMMENTS:       14 pages, 4 figures, 1 table
HIGHLIGHT:      For feature extraction we applied principal component analysis and the feature set size varied from one to 8 principal components.

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

34, TITLE:      Adaptive Interaction Modeling via Graph Operations Search
http://arxiv.org/abs/2005.02113
AUTHORS:        Haoxin Li ; Wei-Shi Zheng ; Yu Tao ; Haifeng Hu ; Jian-Huang Lai
HIGHLIGHT:      In this paper, we automate the process of structures design to learn adaptive structures for interaction modeling.

35, TITLE:      Classification-Based Anomaly Detection for General Data
http://arxiv.org/abs/2005.02359
AUTHORS:        Liron Bergman ; Yedid Hoshen
COMMENTS:       ICLR'20
HIGHLIGHT:      In this work, we present a unifying view and propose an open-set method, GOAD, to relax current generalization assumptions.

36, TITLE:      Manifold Proximal Point Algorithms for Dual Principal Component Pursuit and Orthogonal Dictionary Learning
http://arxiv.org/abs/2005.02356
AUTHORS:        Shixiang Chen ; Zengde Deng ; Shiqian Ma ; Anthony Man-Cho So
HIGHLIGHT:      In this paper, we show how the manifold structure of the sphere can be exploited to design fast algorithms for tackling this problem.

37, TITLE:      Sub-Image Anomaly Detection with Deep Pyramid Correspondences
http://arxiv.org/abs/2005.02357
AUTHORS:        Niv Cohen ; Yedid Hoshen
HIGHLIGHT:      In this work we present a novel anomaly segmentation approach based on alignment between an anomalous image and a constant number of the similar normal images.

38, TITLE:      SLEDGE: A Simple Yet Effective Baseline for Coronavirus Scientific Knowledge Search
http://arxiv.org/abs/2005.02365
AUTHORS:        Sean MacAvaney ; Arman Cohan ; Nazli Goharian
HIGHLIGHT:      In this work, we present a search system called SLEDGE, which utilizes SciBERT to effectively re-rank articles.

39, TITLE:      CODA-19: Reliably Annotating Research Aspects on 10,000+ CORD-19 Abstracts Using Non-Expert Crowd
http://arxiv.org/abs/2005.02367
AUTHORS:        Ting-Hao 'Kenneth' Huang ; Chieh-Yang Huang ; Chien-Kuang Cornelia Ding ; Yen-Chia Hsu ; C. Lee Giles
HIGHLIGHT:      This paper introduces CODA-19, a human-annotated dataset that denotes the Background, Purpose, Method, Finding/Contribution, and Other for 10,966 English abstracts in the COVID-19 Open Research Dataset.

40, TITLE:      Artemis: A Novel Annotation Methodology for Indicative Single Document Summarization
http://arxiv.org/abs/2005.02146
AUTHORS:        Rahul Jha ; Keping Bi ; Yang Li ; Mahdi Pakdaman ; Asli Celikyilmaz ; Ivan Zhibodev ; Kieran McDonald
HIGHLIGHT:      We describe Artemis (Annotation methodology for Rich, Tractable, Extractive, Multi-domain, Indicative Summarization), a novel hierarchical annotation process that produces indicative summaries for documents from multiple domains.

41, TITLE:      Exploring Contextual Word-level Style Relevance for Unsupervised Style Transfer
http://arxiv.org/abs/2005.02049
AUTHORS:        Chulun Zhou ; Liangyu Chen ; Jiachen Liu ; Xinyan Xiao ; Jinsong Su ; Sheng Guo ; Hua Wu
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      In this paper, we propose a novel attentional sequence-to-sequence (Seq2seq) model that dynamically exploits the relevance of each output word to the target style for unsupervised style transfer.

42, TITLE:      Code-switching patterns can be an effective route to improve performance of downstream NLP applications: A case study of humour, sarcasm and hate speech detection
http://arxiv.org/abs/2005.02295
AUTHORS:        Srijan Bansal ; Vishal Garimella ; Ayush Suhane ; Jasabanta Patro ; Animesh Mukherjee
COMMENTS:       This work is accepted as a short paper in the proceedings of ACL 2020
HIGHLIGHT:      In this paper we demonstrate how code-switching patterns can be utilised to improve various downstream NLP applications.

43, TITLE:      NTIRE 2020 Challenge on Video Quality Mapping: Methods and Results
http://arxiv.org/abs/2005.02291
AUTHORS:        Dario Fuoli ; Zhiwu Huang ; Martin Danelljan ; Radu Timofte ; Hua Wang ; Longcun Jin ; Dewei Su ; Jing Liu ; Jaehoon Lee ; Michal Kudelski ; Lukasz Bala ; Dmitry Hrybov ; Marcin Mozejko ; Muchen Li ; Siyao Li ; Bo Pang ; Cewu Lu ; Chao Li ; Dongliang He ; Fu Li ; Shilei Wen
COMMENTS:       The IEEE Conference on Computer Vision and Pattern Recognition (CVPR) Workshops
HIGHLIGHT:      This paper reviews the NTIRE 2020 challenge on video quality mapping (VQM), which addresses the issues of quality mapping from source video domain to target video domain.

44, TITLE:      Establishing Baselines for Text Classification in Low-Resource Languages
http://arxiv.org/abs/2005.02068
AUTHORS:        Jan Christian Blaise Cruz ; Charibeth Cheng
COMMENTS:       We release all our models, finetuning code, and data at https://github.com/jcblaisecruz02/Filipino-Text-Benchmarks
HIGHLIGHT:      In this work, we provide three contributions. First, we introduce two previously unreleased datasets as benchmark datasets for text classification and low-resource multilabel text classification for the low-resource language Filipino. We release all our models and datasets for the research community to use.

45, TITLE:      Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-weighting
http://arxiv.org/abs/2005.02066
AUTHORS:        Dongnan Liu ; Donghao Zhang ; Yang Song ; Fan Zhang ; Lauren O'Donnell ; Heng Huang ; Mei Chen ; Weidong Cai
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this work, we propose a Cycle Consistency Panoptic Domain Adaptive Mask R-CNN (CyC-PDAM) architecture for unsupervised nuclei segmentation in histopathology images, by learning from fluorescence microscopy images.

46, TITLE:      Encoding Linear Constraints into SAT
http://arxiv.org/abs/2005.02073
AUTHORS:        Ignasi Abío ; Valentin Mayer-Eichberger ; Peter Stuckey
HIGHLIGHT:      In this paper we explore and categorize SAT encodings for linear integer constraints.

47, TITLE:      Explainable AI for Classification using Probabilistic Logic Inference
http://arxiv.org/abs/2005.02074
AUTHORS:        Xiuyi Fan ; Siyuan Liu ; Thomas C. Henderson
HIGHLIGHT:      In this work, we present an explainable classification method.

48, TITLE:      Self-organizing Pattern in Multilayer Network for Words and Syllables
http://arxiv.org/abs/2005.02087
AUTHORS:        Li-Min Wang ; Sun-Ting Tsai ; Shan-Jyun Wu ; Meng-Xue Tsai ; Daw-Wei Wang ; Yi-Ching Su ; Tzay-Ming Hong
COMMENTS:       8 pages, 5 figures
HIGHLIGHT:      We discover the multi-layer network for words and syllables based on this analysis exhibits the feature of self-organization which relies heavily on the inclusion of syllables and their connections.

49, TITLE:      Superposition for Lambda-Free Higher-Order Logic
http://arxiv.org/abs/2005.02094
AUTHORS:        Alexander Bentkamp ; Jasmin Blanchette ; Simon Cruanes ; Uwe Waldmann
HIGHLIGHT:      We introduce refutationally complete superposition calculi for intentional and extensional clausal $\lambda$-free higher-order logic, two formalisms that allow partial application and applied variables.

50, TITLE:      Enabling Deep Spiking Neural Networks with Hybrid Conversion and Spike Timing Dependent Backpropagation
http://arxiv.org/abs/2005.01807
AUTHORS:        Nitin Rathi ; Gopalakrishnan Srinivasan ; Priyadarshini Panda ; Kaushik Roy
COMMENTS:       International Conference on Learning Representations (ICLR), 2020 https://openreview.net/forum?id=B1xSperKvH&noteId=B1xSperKvH
HIGHLIGHT:      To address these challenges, we present a computationally-efficient training technique for deep SNNs.

51, TITLE:      Semi-supervised lung nodule retrieval
http://arxiv.org/abs/2005.01805
AUTHORS:        Mark Loyman ; Hayit Greenspan
HIGHLIGHT:      The current study suggests a semi-supervised approach that involves two steps: 1) Automatic annotation of a given partially labeled dataset; 2) Learning a semantic similarity metric space based on the predicated annotations.

52, TITLE:      A Systematic Media Frame Analysis of 1.5 Million New York Times Articles from 2000 to 2017
http://arxiv.org/abs/2005.01803
AUTHORS:        Haewoon Kwak ; Jisun An ; Yong-Yeol Ahn
COMMENTS:       10pages, WebSci'20
HIGHLIGHT:      By examining specific topics and sentiments, we identify characteristics and dynamics of each frame.

53, TITLE:      Learning-based Tracking of Fast Moving Objects
http://arxiv.org/abs/2005.01802
AUTHORS:        Ales Zita ; Filip Sroubek
HIGHLIGHT:      In this paper, we present a tracking-by-segmentation approach implemented using state-of-the-art deep learning methods that performs near-realtime tracking on real-world video sequences.

54, TITLE:      Spying on your neighbors: Fine-grained probing of contextual embeddings for information about surrounding words
http://arxiv.org/abs/2005.01810
AUTHORS:        Josef Klafka ; Allyson Ettinger
COMMENTS:       ACL 2020
HIGHLIGHT:      To address this question, we introduce a suite of probing tasks that enable fine-grained testing of contextual embeddings for encoding of information about surrounding words.

55, TITLE:      Understanding Scanned Receipts
http://arxiv.org/abs/2005.01828
AUTHORS:        Eric Melz
COMMENTS:       8 pages, 3 figures, no conference submission
HIGHLIGHT:      In this paper, we focus on the task of Named Entity Linking (NEL) of scanned receipt line items; specifically, the task entails associating shorthand text from OCR'd receipts with a knowledge base (KB) of grocery products.

56, TITLE:      Exploring Controllable Text Generation Techniques
http://arxiv.org/abs/2005.01822
AUTHORS:        Shrimai Prabhumoye ; Alan W Black ; Ruslan Salakhutdinov
HIGHLIGHT:      In this work, we provide a new schema of the pipeline of the generation process by classifying it into five modules.

57, TITLE:      Evaluating Explainable AI: Which Algorithmic Explanations Help Users Predict Model Behavior?
http://arxiv.org/abs/2005.01831
AUTHORS:        Peter Hase ; Mohit Bansal
COMMENTS:       ACL 2020 (13 pages)
HIGHLIGHT:      Through two kinds of simulation tests involving text and tabular data, we evaluate five explanations methods: (1) LIME, (2) Anchor, (3) Decision Boundary, (4) a Prototype model, and (5) a Composite approach that combines explanations from each method.

58, TITLE:      Exploring Content Selection in Summarization of Novel Chapters
http://arxiv.org/abs/2005.01840
AUTHORS:        Faisal Ladhak ; Bryan Li ; Yaser Al-Onaizan ; Kathleen McKeown
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We present a new summarization task, generating summaries of novel chapters using summary/chapter pairs from online study guides.

59, TITLE:      Designing Data Augmentation for Simulating Interventions
http://arxiv.org/abs/2005.01856
AUTHORS:        Maximilian Ilse ; Jakub M. Tomczak ; Patrick Forré
HIGHLIGHT:      In this paper, we focus on the case where the problem arises through spurious correlation between the observed domains and the actual task labels.

60, TITLE:      Data Augmentation for Hypernymy Detection
http://arxiv.org/abs/2005.01854
AUTHORS:        Thomas Kober ; Julie Weeds ; Lorenzo Bertolini ; David Weir
HIGHLIGHT:      We have developed two novel data augmentation techniques which generate new training examples from existing ones.

61, TITLE:      Envy-free cake cutting: A polynomial number of queries with high probability
http://arxiv.org/abs/2005.01982
AUTHORS:        Guillaume Chèze
HIGHLIGHT:      In this article we propose a probabilistic framework in order to study the fair division of a divisible good, e.g. a cake, between n players.

62, TITLE:      Post-hoc explanation of black-box classifiers using confident itemsets
http://arxiv.org/abs/2005.01992
AUTHORS:        Milad Moradi ; Matthias Samwald
HIGHLIGHT:      In this paper, we address the above challenges by proposing an explanation method named Confident Itemsets Explanation (CIE).

63, TITLE:      NTIRE 2020 Challenge on Real-World Image Super-Resolution: Methods and Results
http://arxiv.org/abs/2005.01996
AUTHORS:        Andreas Lugmayr ; Martin Danelljan ; Radu Timofte ; Namhyuk Ahn ; Dongwoon Bai ; Jie Cai ; Yun Cao ; Junyang Chen ; Kaihua Cheng ; SeYoung Chun ; Wei Deng ; Mostafa El-Khamy ; Chiu Man Ho ; Xiaozhong Ji ; Amin Kheradmand ; Gwantae Kim ; Hanseok Ko ; Kanghyu Lee ; Jungwon Lee ; Hao Li ; Ziluan Liu ; Zhi-Song Liu ; Shuai Liu ; Yunhua Lu ; Zibo Meng ; Pablo Navarrete Michelini ; Christian Micheloni ; Kalpesh Prajapati ; Haoyu Ren ; Yong Hyeok Seo ; Wan-Chi Siu ; Kyung-Ah Sohn ; Ying Tai ; Rao Muhammad Umer ; Shuangquan Wang ; Huibing Wang ; Timothy Haoning Wu ; Haoning Wu ; Biao Yang ; Fuzhi Yang ; Jaejun Yoo ; Tongtong Zhao ; Yuanbo Zhou ; Haijie Zhuo ; Ziyao Zong ; Xueyi Zou
HIGHLIGHT:      This paper reviews the NTIRE 2020 challenge on real world super-resolution.

64, TITLE:      HOG, LBP and SVM based Traffic Density Estimation at Intersection
http://arxiv.org/abs/2005.01770
AUTHORS:        Devashish Prasad ; Kshitij Kapadni ; Ayan Gadpal ; Manish Visave ; Kavita Sultanpure
COMMENTS:       paper accepted at IEEE PuneCon 2019
HIGHLIGHT:      We tend to propose an efficient way to estimate the traffic density on intersection using image processing and machine learning techniques in real time.

65, TITLE:      ADVISER: A Toolkit for Developing Multi-modal, Multi-domain and Socially-engaged Conversational Agents
http://arxiv.org/abs/2005.01777
AUTHORS:        Chia-Yu Li ; Daniel Ortega ; Dirk Väth ; Florian Lux ; Lindsey Vanderlyn ; Maximilian Schmidt ; Michael Neumann ; Moritz Völkel ; Pavel Denisov ; Sabrina Jenne ; Zorica Kacarevic ; Ngoc Thang Vu
COMMENTS:       All authors contributed equally. Accepted to be presented at ACL - System demonstrations - 2020
HIGHLIGHT:      We present ADVISER - an open-source, multi-domain dialog system toolkit that enables the development of multi-modal (incorporating speech, text and vision), socially-engaged (e.g. emotion recognition, engagement level prediction and backchanneling) conversational agents.

66, TITLE:      Discrete Optimization for Unsupervised Sentence Summarization with Word-Level Extraction
http://arxiv.org/abs/2005.01791
AUTHORS:        Raphael Schumann ; Lili Mou ; Yao Lu ; Olga Vechtomova ; Katja Markert
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      We model these two aspects in an unsupervised objective function, consisting of language modeling and semantic similarity metrics.

67, TITLE:      Generating SOAP Notes from Doctor-Patient Conversations
http://arxiv.org/abs/2005.01795
AUTHORS:        Kundan Krishna ; Sopan Khosla ; Jeffrey P. Bigham ; Zachary C. Lipton
HIGHLIGHT:      In this paper, we present the first study to evaluate complete pipelines for leveraging these transcripts to train machine learning model to generate these notes.

68, TITLE:      Recurrent Neural Network Learning of Performance and Intrinsic Population Dynamics from Sparse Neural Data
http://arxiv.org/abs/2005.02211
AUTHORS:        Alessandro Salatiello ; Martin A. Giese
HIGHLIGHT:      In this work, we introduce a novel training strategy that allows learning not only the input-output behavior of an RNN but also its internal network dynamics, based on sparse neural recordings.

69, TITLE:      A Survey on Dialog Management: Recent Advances and Challenges
http://arxiv.org/abs/2005.02233
AUTHORS:        Yinpei Dai ; Huihua Yu ; Yixuan Jiang ; Chengguang Tang ; Yongbin Li ; Jian Sun
HIGHLIGHT:      In this paper, we survey recent advances and challenges within three critical topics for DM: (1) improving model scalability to facilitate dialog system modeling in new scenarios, (2) dealing with the data scarcity problem for dialog policy learning, and (3) enhancing the training efficiency to achieve better task-completion performance .

70, TITLE:      Query Reformulation using Query History for Passage Retrieval in Conversational Search
http://arxiv.org/abs/2005.02230
AUTHORS:        Sheng-Chieh Lin ; Jheng-Hong Yang ; Rodrigo Nogueira ; Ming-Feng Tsai ; Chuan-Ju Wang ; Jimmy Lin
COMMENTS:       11 pages
HIGHLIGHT:      To address this problem, we present an effective multi-stage pipeline for passage ranking in conversational search that integrates a widely-used IR system with a conversational query reformulation module.

71, TITLE:      Lower Bounds for Semi-adaptive Data Structures via Corruption
http://arxiv.org/abs/2005.02238
AUTHORS:        Pavel Dvořák ; Bruno Loff
COMMENTS:       14 pages
HIGHLIGHT:      We generalize the lower bound of Ko and Weinstein to work not just for the Disjointness, but for any function having high complexity under the smooth corruption bound.

72, TITLE:      Creating a Multimodal Dataset of Images and Text to Study Abusive Language
http://arxiv.org/abs/2005.02235
AUTHORS:        Alessio Palmero Aprosio ; Stefano Menini ; Sara Tonelli
HIGHLIGHT:      We find that users judge the same images in different ways, although the presence of a person in the picture increases the probability to get an offensive comment.

73, TITLE:      On Interpretability of Deep Learning based Skin Lesion Classifiers using Concept Activation Vectors
http://arxiv.org/abs/2005.02000
AUTHORS:        Adriano Lucieri ; Muhammad Naseer Bajwa ; Stephan Alexander Braun ; Muhammad Imran Malik ; Andreas Dengel ; Sheraz Ahmed
COMMENTS:       Accepted for the IEEE International Joint Conference on Neural Networks (IJCNN) 2020
HIGHLIGHT:      This work aims at elucidating a deep learning based medical image classifier by verifying that the model learns and utilizes similar disease-related concepts as described and employed by dermatologists.

74, TITLE:      Dynamically Adjusting Transformer Batch Size by Monitoring Gradient Direction Change
http://arxiv.org/abs/2005.02008
AUTHORS:        Hongfei Xu ; Josef van Genabith ; Deyi Xiong ; Qiuhui Liu
HIGHLIGHT:      To improve the efficiency of our approach for large models, we propose a sampling approach to select gradients of parameters sensitive to the batch size.

75, TITLE:      P2ExNet: Patch-based Prototype Explanation Network
http://arxiv.org/abs/2005.02006
AUTHORS:        Dominique Mercier ; Andreas Dengel ; Sheraz Ahmed
COMMENTS:       12 pages (11 + 1 references), 7 figures
HIGHLIGHT:      Addressing the need for an explainable approach, we propose a novel interpretable network scheme, designed to inherently use an explainable reasoning process inspired by the human cognition without the need of additional post-hoc explainability methods.

76, TITLE:      A Linear Algebra Approach to Linear Metatheory
http://arxiv.org/abs/2005.02247
AUTHORS:        James Wood ; Robert Atkey
COMMENTS:       8 pages, 1 figure, submitted to Linearity/TLLA
HIGHLIGHT:      We present a methodology based on linear algebra over semirings, extending McBride's kits and traversals approach for the metatheory of syntax with binding to linear usage-annotated terms.

77, TITLE:      Neural Syntactic Preordering for Controlled Paraphrase Generation
http://arxiv.org/abs/2005.02013
AUTHORS:        Tanya Goyal ; Greg Durrett
COMMENTS:       ACL 2020 camera ready
HIGHLIGHT:      Our work, inspired by pre-ordering literature in machine translation, uses syntactic transformations to softly "reorder'' the source sentence and guide our neural paraphrasing model.

78, TITLE:      Learning programs by learning from failures
http://arxiv.org/abs/2005.02259
AUTHORS:        Andrew Cropper ; Rolf Morel
COMMENTS:       Under review
HIGHLIGHT:      We introduce learning programs by learning from failures.

79, TITLE:      AGE Challenge: Angle Closure Glaucoma Evaluation in Anterior Segment Optical Coherence Tomography
http://arxiv.org/abs/2005.02258
AUTHORS:        Huazhu Fu ; Fei Li ; Xu Sun ; Xingxing Cao ; Jingan Liao ; Jose Ignacio Orlando ; Xing Tao ; Yuexiang Li ; Shihao Zhang ; Mingkui Tan ; Chenglang Yuan ; Cheng Bian ; Ruitao Xie ; Jiongcheng Li ; Xiaomeng Li ; Jing Wang ; Le Geng ; Panming Li ; Huaying Hao ; Jiang Liu ; Yan Kong ; Yongyong Ren ; Hrvoje Bogunovic ; Xiulan Zhang ; Yanwu Xu
COMMENTS:       AGE Challenge website at: https://age.grand-challenge.org
HIGHLIGHT:      In this paper, we summarize these eight onsite challenge methods and analyze their corresponding results in the two tasks. For this challenge, we released a large data of 4800 annotated AS-OCT images from 199 patients, and also proposed an evaluation framework to benchmark and compare different models.

80, TITLE:      LandCover.ai: Dataset for Automatic Mapping of Buildings, Woodlands and Water from Aerial Imagery
http://arxiv.org/abs/2005.02264
AUTHORS:        Adrian Boguszewski ; Dominik Batorski ; Natalia Ziemba-Jankowska ; Anna Zambrzycka ; Tomasz Dziedzic
HIGHLIGHT:      Here we introduce LandCover.ai (Land Cover from Aerial Imagery) dataset that propose semantic segmentation.

81, TITLE:      Global explanations for discovering bias in data
http://arxiv.org/abs/2005.02269
AUTHORS:        Agnieszka Mikołajczyk ; Michał Grochowski ; Arkadiusz Kwasigroch
COMMENTS:       9 pages, 4 figures, code available
HIGHLIGHT:      In the paper, we propose attention-based summarized post-hoc explanations for detection and identification of bias in data.



==========Updates to Previous Papers==========
1, TITLE:       Soft-Label Dataset Distillation and Text Dataset Distillation
http://arxiv.org/abs/1910.02551
AUTHORS:        Ilia Sucholutsky ; Matthias Schonlau
HIGHLIGHT:      We propose to simultaneously distill both images and their labels, thus assigning each synthetic sample a `soft' label (a distribution of labels).

2, TITLE:       A Survey of Learning Causality with Data: Problems and Methods
http://arxiv.org/abs/1809.09337
AUTHORS:        Ruocheng Guo ; Lu Cheng ; Jundong Li ; P. Richard Hahn ; Huan Liu
COMMENTS:       35 pages, accepted by ACM CSUR
HIGHLIGHT:      To answer this question, this survey provides a comprehensive and structured review of both traditional and frontier methods in learning causality and relations along with the connections between causality and machine learning.

3, TITLE:       Confederated Machine Learning on Horizontally and Vertically Separated Medical Data for Large-Scale Health System Intelligence
http://arxiv.org/abs/1910.02109
AUTHORS:        Dianbo Liu ; Timothy A Miller ; Kenneth D. Mandl
HIGHLIGHT:      We proposed and evaluated a confederated learning to training machine learning model to stratify the risk of several diseases among when data are horizontally separated by individual, vertically separated by data type, and separated by identity without patient ID matching.

4, TITLE:       Revisiting Simple Domain Adaptation Methods in Unsupervised Neural Machine Translation
http://arxiv.org/abs/1908.09605
AUTHORS:        Haipeng Sun ; Rui Wang ; Kehai Chen ; Masao Utiyama ; Eiichiro Sumita ; Tiejun Zhao ; Chenhui Chu
HIGHLIGHT:      In this work, we empirically show different scenarios for unsupervised neural machine translation.

5, TITLE:       6DoF Object Pose Estimation via Differentiable Proxy Voting Loss
http://arxiv.org/abs/2002.03923
AUTHORS:        Xin Yu ; Zheyu Zhuang ; Piotr Koniusz ; Hongdong Li
HIGHLIGHT:      In this paper, we aim to reduce such errors by incorporating the distances between pixels and keypoints into our objective.

6, TITLE:       The Maximum Entropy on the Mean Method for Image Deblurring
http://arxiv.org/abs/2002.10434
AUTHORS:        Gabriel Rioux ; Rustum Choksi ; Tim Hoheisel ; Christopher Scarvelis
COMMENTS:       23 pages, 7 figures
HIGHLIGHT:      We propose an alternative approach, shifting the paradigm towards regularization at the level of the probability distribution on the space of images.

7, TITLE:       Pores for thought: The use of generative adversarial networks for the stochastic reconstruction of 3D multi-phase electrode microstructures with periodic boundaries
http://arxiv.org/abs/2003.11632
AUTHORS:        Andrea Gayon-Lombardo ; Lukas Mosser ; Nigel P. Brandon ; Samuel J. Cooper
COMMENTS:       37 pages, 10 figures
HIGHLIGHT:      This work implements a deep convolutional generative adversarial network (DC-GAN) for generating realistic n-phase microstructural data.

8, TITLE:       Deep Eyedentification: Biometric Identification using Micro-Movements of the Eye
http://arxiv.org/abs/1906.11889
AUTHORS:        Lena A. Jäger ; Silvia Makowski ; Paul Prasse ; Sascha Liehr ; Maximilian Seidler ; Tobias Scheffer
HIGHLIGHT:      We study involuntary micro-movements of the eye for biometric identification.

9, TITLE:       Towards Accurate and Robust Domain Adaptation under Noisy Environments
http://arxiv.org/abs/2004.12529
AUTHORS:        Zhongyi Han ; Xian-Jin Gui ; Chaoran Cui ; Yilong Yin
COMMENTS:       To appear in Proceedings of IJCAI 2020
HIGHLIGHT:      In this paper, we report our attempt towards achieving accurate noise-robust domain adaptation.

10, TITLE:      Improving Truthfulness of Headline Generation
http://arxiv.org/abs/2005.00882
AUTHORS:        Kazuki Matsumaru ; Sho Takase ; Naoaki Okazaki
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      This paper explores improving the truthfulness in headline generation on two popular datasets.

11, TITLE:      Assessing Car Damage using Mask R-CNN
http://arxiv.org/abs/2004.14173
AUTHORS:        Sarath P ; Soorya M ; Shaik Abdul Rahman A ; S Suresh Kumar ; K Devaki
HIGHLIGHT:      In this paper we consider the issue of vehicle harm characterization, where a portion of the classifications can be fine-granular.

12, TITLE:      nuScenes: A multimodal dataset for autonomous driving
http://arxiv.org/abs/1903.11027
AUTHORS:        Holger Caesar ; Varun Bankiti ; Alex H. Lang ; Sourabh Vora ; Venice Erin Liong ; Qiang Xu ; Anush Krishnan ; Yu Pan ; Giancarlo Baldan ; Oscar Beijbom
COMMENTS:       CVPR 2020 camera ready incl. supplementary material
HIGHLIGHT:      In this work we present nuTonomy scenes (nuScenes), the first dataset to carry the full autonomous vehicle sensor suite: 6 cameras, 5 radars and 1 lidar, all with full 360 degree field of view.

13, TITLE:      End-to-End Learning of Visual Representations from Uncurated Instructional Videos
http://arxiv.org/abs/1912.06430
AUTHORS:        Antoine Miech ; Jean-Baptiste Alayrac ; Lucas Smaira ; Ivan Laptev ; Josef Sivic ; Andrew Zisserman
COMMENTS:       CVPR'2020 Oral
HIGHLIGHT:      In this work we propose a new learning approach, MIL-NCE, capable of addressing misalignments inherent to narrated videos.

14, TITLE:      Defocus Deblurring Using Dual-Pixel Data
http://arxiv.org/abs/2005.00305
AUTHORS:        Abdullah Abuolaim ; Michael S. Brown
HIGHLIGHT:      We propose an effective defocus deblurring method that exploits data available on dual-pixel (DP) sensors found on most modern cameras.

15, TITLE:      Ego-motion and Surrounding Vehicle State Estimation Using a Monocular Camera
http://arxiv.org/abs/2005.01632
AUTHORS:        Jun Hayakawa ; Behzad Dariush
HIGHLIGHT:      In this paper, we propose a novel machine learning method to estimate ego-motion and surrounding vehicle state using a single monocular camera.

16, TITLE:      Something-Else: Compositional Action Recognition with Spatial-Temporal Interaction Networks
http://arxiv.org/abs/1912.09930
AUTHORS:        Joanna Materzynska ; Tete Xiao ; Roei Herzig ; Huijuan Xu ; Xiaolong Wang ; Trevor Darrell
HIGHLIGHT:      In this paper, we study the compositionality of action by looking into the dynamics of subject-object interactions.

17, TITLE:      CDL: Curriculum Dual Learning for Emotion-Controllable Response Generation
http://arxiv.org/abs/2005.00329
AUTHORS:        Lei Shen ; Yang Feng
COMMENTS:       To appear at ACL 2020 (long paper)
HIGHLIGHT:      To alleviate these problems, we propose a novel framework named Curriculum Dual Learning (CDL) which extends the emotion-controllable response generation to a dual task to generate emotional responses and emotional queries alternatively.

18, TITLE:      Linguistic Typology Features from Text: Inferring the Sparse Features of World Atlas of Language Structures
http://arxiv.org/abs/2005.00100
AUTHORS:        Alexander Gutkin ; Tatiana Merkulova ; Martin Jansche
COMMENTS:       Originally prepared as a conference submission to EMNLP 2018
HIGHLIGHT:      In this paper we investigate whether the various linguistic features from World Atlas of Language Structures (WALS) can be reliably inferred from multi-lingual text.

19, TITLE:      Uncertain Natural Language Inference
http://arxiv.org/abs/1909.03042
AUTHORS:        Tongfei Chen ; Zhengping Jiang ; Adam Poliak ; Keisuke Sakaguchi ; Benjamin Van Durme
COMMENTS:       Accepted to ACL 2020
HIGHLIGHT:      We describe a direct scalar regression modeling approach, and find that existing categorically labeled NLI data can be used in pre-training.

20, TITLE:      An Accurate Model for Predicting the (Graded) Effect of Context in Word Similarity Based on Bert
http://arxiv.org/abs/2005.01006
AUTHORS:        Wei Bao ; Hongshu Che ; Jiandong Zhang
HIGHLIGHT:      We apply several methods in calculating the distance between two embedding vector generated by Bidirectional Encoder Representation from Transformer (BERT).

21, TITLE:      Self-Supervised training for blind multi-frame video denoising
http://arxiv.org/abs/2004.06957
AUTHORS:        Valéry Dewil ; Jérémy Anger ; Axel Davy ; Thibaud Ehret ; Pablo Arias ; Gabriele Facciolo
COMMENTS:       14 pages
HIGHLIGHT:      We propose a self-supervised approach for training multi-frame video denoising networks.

22, TITLE:      Joint Reasoning for Multi-Faceted Commonsense Knowledge
http://arxiv.org/abs/2001.04170
AUTHORS:        Yohan Chalier ; Simon Razniewski ; Gerhard Weikum
COMMENTS:       11 pages
HIGHLIGHT:      This paper aims to overcome these limitations by introducing a multi-faceted model of CSK statements and methods for joint reasoning over sets of inter-related statements.

23, 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.

24, TITLE:      XtremeDistil: Multi-stage Distillation for Massive Multilingual Models
http://arxiv.org/abs/2004.05686
AUTHORS:        Subhabrata Mukherjee ; Ahmed Awadallah
COMMENTS:       To appear in ACL 2020
HIGHLIGHT:      In this work we study knowledge distillation with a focus on multi-lingual Named Entity Recognition (NER).

25, TITLE:      Object-Centric Task and Motion Planning in Dynamic Environments
http://arxiv.org/abs/1911.04679
AUTHORS:        Toki Migimatsu ; Jeannette Bohg
HIGHLIGHT:      We propose a TAMP algorithm that optimizes over Cartesian frames defined relative to target objects.

26, TITLE:      Lipschitz Constrained Parameter Initialization for Deep Transformers
http://arxiv.org/abs/1911.03179
AUTHORS:        Hongfei Xu ; Qiuhui Liu ; Josef van Genabith ; Deyi Xiong ; Jingyi Zhang
HIGHLIGHT:      The Transformer translation model employs residual connection and layer normalization to ease the optimization difficulties caused by its multi-layer encoder/decoder structure.

27, TITLE:      Foundations of Structural Causal Models with Cycles and Latent Variables
http://arxiv.org/abs/1611.06221
AUTHORS:        Stephan Bongers ; Patrick Forré ; Jonas Peters ; Bernhard Schölkopf ; Joris M. Mooij
COMMENTS:       Under submission in the Annals of Statistics
HIGHLIGHT:      In this paper, we investigate SCMs in a more general setting, allowing for the presence of both latent confounders and cycles.

28, TITLE:      Disentangled Image Generation Through Structured Noise Injection
http://arxiv.org/abs/2004.12411
AUTHORS:        Yazeed Alharbi ; Peter Wonka
COMMENTS:       CVPR2020 Oral. Project page: https://github.com/yalharbi/StructuredNoiseInjection
HIGHLIGHT:      Instead of traditional approaches, we propose feeding multiple noise codes through separate fully-connected layers respectively.

29, TITLE:      Distance Guided Channel Weighting for Semantic Segmentation
http://arxiv.org/abs/2004.12679
AUTHORS:        Lanyun Zhu ; Shiping Zhu ; Xuanyi Liu ; Li Luo
HIGHLIGHT:      In this paper, we address above issue by introducing the Distance Guided Channel Weighting (DGCW) Module.

30, TITLE:      Head-Driven Phrase Structure Grammar Parsing on Penn Treebank
http://arxiv.org/abs/1907.02684
AUTHORS:        Junru Zhou ; Hai Zhao
COMMENTS:       Accepted by ACL 2019
HIGHLIGHT:      In details, we report 96.33 F1 of constituent parsing and 97.20\% UAS of dependency parsing on PTB.

31, TITLE:      An analysis of the utility of explicit negative examples to improve the syntactic abilities of neural language models
http://arxiv.org/abs/2004.02451
AUTHORS:        Hiroshi Noji ; Hiroya Takamura
COMMENTS:       ACL 2020 camera ready (long paper); code is available at https://github.com/aistairc/lm_syntax_negative
HIGHLIGHT:      We explore the utilities of explicit negative examples in training neural language models.

32, TITLE:      Transformers as Soft Reasoners over Language
http://arxiv.org/abs/2002.05867
AUTHORS:        Peter Clark ; Oyvind Tafjord ; Kyle Richardson
COMMENTS:       IJCAI 2020
HIGHLIGHT:      This paper investigates a modern approach to this problem where the facts and rules are provided as natural language sentences, thus bypassing a formal representation.

33, TITLE:      Computer Vision For COVID-19 Control: A Survey
http://arxiv.org/abs/2004.09420
AUTHORS:        Anwaar Ulhaq ; Asim Khan ; Douglas Gomes ; Manoranjan Paul
COMMENTS:       24 Pages, 9 Figures
HIGHLIGHT:      It motivated us to review the recent work, collect information about available research resources and an indication of future research directions.

34, TITLE:      Multi-Perspective, Simultaneous Embedding
http://arxiv.org/abs/1909.06485
AUTHORS:        Md Iqbal Hossain ; Vahan Huroyan ; Stephen Kobourov ; Raymundo Navarrete
HIGHLIGHT:      We describe MPSE: a Multi-Perspective Simultaneous Embedding method for visualizing high-dimensional data, based on multiple pairwise distances between the data points.

35, TITLE:      Pseudo-healthy synthesis with pathology disentanglement and adversarial learning
http://arxiv.org/abs/2005.01607
AUTHORS:        Tian Xia ; Agisilaos Chartsias ; Sotirios A. Tsaftaris
COMMENTS:       This paper has been accepted by Medical Image Analysis
HIGHLIGHT:      In this paper, we present a model that is encouraged to disentangle the information of pathology from what seems to be healthy.

36, TITLE:      CoMoGCN: Coherent Motion Aware Trajectory Prediction with Graph Representation
http://arxiv.org/abs/2005.00754
AUTHORS:        Yuying Chen ; Congcong Liu ; Bertram Shi ; Ming Liu
COMMENTS:       12 pages, 3 figures
HIGHLIGHT:      In this work, we propose a novel framework, coherent motion aware graph convolutional network (CoMoGCN), for trajectory prediction in crowded scenes with group constraints.

37, TITLE:      Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional Neural Network (R-CNN)
http://arxiv.org/abs/2004.12880
AUTHORS:        Vittorio Mazzia ; Aleem Khaliq ; Marcello Chiaberge
HIGHLIGHT:      In this paper, a novel and optimal deep learning model for pixel-based LC&CC is developed and implemented based on Recurrent Neural Networks (RNN) in combination with Convolutional Neural Networks (CNN) using multi-temporal sentinel-2 imagery of central north part of Italy, which has diverse agricultural system dominated by economic crop types.

38, TITLE:      Can We Learn Heuristics For Graphical Model Inference Using Reinforcement Learning?
http://arxiv.org/abs/2005.01508
AUTHORS:        Safa Messaoud ; Maghav Kumar ; Alexander G. Schwing
COMMENTS:       CVPR 2020 (Oral)
HIGHLIGHT:      In this paper, we show that we can learn program heuristics, i.e., policies, for solving inference in higher order CRFs for the task of semantic segmentation, using reinforcement learning.

39, TITLE:      RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
http://arxiv.org/abs/1911.04942
AUTHORS:        Bailin Wang ; Richard Shin ; Xiaodong Liu ; Oleksandr Polozov ; Matthew Richardson
COMMENTS:       ACL 2020 camera-ready; 12 pages, 5 figures, 7 tables. arXiv admin note: text overlap with arXiv:1906.11790
HIGHLIGHT:      We present a unified framework, based on the relation-aware self-attention mechanism, to address schema encoding, schema linking, and feature representation within a text-to-SQL encoder.

40, TITLE:      Not All Claims are Created Equal: Choosing the Right Statistical Approach to Assess Hypotheses
http://arxiv.org/abs/1911.03850
AUTHORS:        Erfan Sadeqi Azer ; Daniel Khashabi ; Ashish Sabharwal ; Dan Roth
COMMENTS:       ACL 2020
HIGHLIGHT:      Empirical research in Natural Language Processing (NLP) has adopted a narrow set of principles for assessing hypotheses, relying mainly on p-value computation, which suffers from several known issues.

41, TITLE:      Discriminative Pattern Mining for Breast Cancer Histopathology Image Classification via Fully Convolutional Autoencoder
http://arxiv.org/abs/1902.08670
AUTHORS:        Xingyu Li ; Marko Radulovic ; Ksenija Kanjer ; Konstantinos N. Plataniotis
HIGHLIGHT:      In this paper, we propose a practical and self-interpretable invasive cancer diagnosis solution.

42, TITLE:      MagnifierNet: Towards Semantic Adversary and Fusion for Person Re-identification
http://arxiv.org/abs/2002.10979
AUTHORS:        Yushi Lan ; Yuan Liu ; Maoqing Tian ; Xinchi Zhou ; Xuesen Zhang ; Shuai Yi ; Hongsheng Li
HIGHLIGHT:      In this work, we propose MagnifierNet, a triple-branch network which accurately mines details from whole to parts.

43, TITLE:      ASNets: Deep Learning for Generalised Planning
http://arxiv.org/abs/1908.01362
AUTHORS:        Sam Toyer ; Felipe Trevizan ; Sylvie Thiébaux ; Lexing Xie
COMMENTS:       Journal extension of AAAI'18 paper (arXiv:1709.04271)
HIGHLIGHT:      In this paper, we discuss the learning of generalised policies for probabilistic and classical planning problems using Action Schema Networks (ASNets).

44, TITLE:      PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling
http://arxiv.org/abs/2003.00492
AUTHORS:        Xu Yan ; Chaoda Zheng ; Zhen Li ; Sheng Wang ; Shuguang Cui
COMMENTS:       To appear in CVPR 2020. Also seen in http://kaldir.vc.in.tum.de/scannet_benchmark/
HIGHLIGHT:      In this paper, we present a novel end-to-end network for robust point clouds processing, named PointASNL, which can deal with point clouds with noise effectively.

45, TITLE:      Super Resolution for Root Imaging
http://arxiv.org/abs/2003.13537
AUTHORS:        Jose F. Ruiz-Munoz ; Jyothier K. Nimmagadda ; Tyler G. Dowd ; James E. Baciak ; Alina Zare
COMMENTS:       Under review. Submitted to Applications in Plant Sciences (APPS)
HIGHLIGHT:      We propose a SR framework for enhancing images of plant roots by using convolutional neural networks (CNNs).

46, TITLE:      TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
http://arxiv.org/abs/1909.06892
AUTHORS:        Shubham Jain ; Sumeet Kumar Gupta ; Anand Raghunathan
COMMENTS:       12 pages, 18 figures, Accepted in IEEE Transactions on Very Large Scale Integration (VLSI) Systems 2020
HIGHLIGHT:      We propose TiM-DNN, a programmable in-memory accelerator that is specifically designed to execute ternary DNNs.

47, TITLE:      Comparison of Image Quality Models for Optimization of Image Processing Systems
http://arxiv.org/abs/2005.01338
AUTHORS:        Keyan Ding ; Kede Ma ; Shiqi Wang ; Eero P. Simoncelli
HIGHLIGHT:      Perceptual datasets (e.g., LIVE and TID2013) gathered for this purpose provide useful benchmarks for improving IQA methods, but their heavy use creates a risk of overfitting.

48, TITLE:      Structured Tuning for Semantic Role Labeling
http://arxiv.org/abs/2005.00496
AUTHORS:        Tao Li ; Parth Anand Jawale ; Martha Palmer ; Vivek Srikumar
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      In this paper, we present a structured tuning framework to improve models using softened constraints only at training time.

49, TITLE:      Salient Slices: Improved Neural Network Training and Performance with Image Entropy
http://arxiv.org/abs/1907.12436
AUTHORS:        Steven J. Frank ; Andrea M. Frank
COMMENTS:       Final version; article will be published in Neural Computation 32, 1222-1237 (June 2020)
HIGHLIGHT:      In particular, we utilize image entropy as the diversity criterion.

50, TITLE:      Deepfakes Detection with Automatic Face Weighting
http://arxiv.org/abs/2004.12027
AUTHORS:        Daniel Mas Montserrat ; Hanxiang Hao ; S. K. Yarlagadda ; Sriram Baireddy ; Ruiting Shao ; János Horváth ; Emily Bartusiak ; Justin Yang ; David Güera ; Fengqing Zhu ; Edward J. Delp
HIGHLIGHT:      In this paper, we introduce a method based on convolutional neural networks (CNNs) and recurrent neural networks (RNNs) that extracts visual and temporal features from faces present in videos to accurately detect manipulations.

51, TITLE:      Quasi-popular Matchings, Optimality, and Extended Formulations
http://arxiv.org/abs/1904.05974
AUTHORS:        Yuri Faenza ; Telikepalli Kavitha
HIGHLIGHT:      Our goal is to efficiently compute a matching of cost at most opt by paying the price of mildly relaxing popularity.

52, TITLE:      Vanishing Point Detection with Direct and Transposed Fast Hough Transform inside the neural network
http://arxiv.org/abs/2002.01176
AUTHORS:        A. Sheshkus ; A. Chirvonaya ; D. Matveev ; D. Nikolaev ; V. L. Arlazarov
COMMENTS:       9 pages, 9 figures, submitted to "Computer Optics"; extra experiment added, new theorem proof added, references added
HIGHLIGHT:      In this paper, we suggest a new neural network architecture for vanishing point detection in images.

53, TITLE:      Deep Learning COVID-19 Features on CXR using Limited Training Data Sets
http://arxiv.org/abs/2004.05758
AUTHORS:        Yujin Oh ; Sangjoon Park ; Jong Chul Ye
COMMENTS:       Accepted for IEEE Trans. on Medical Imaging Special Issue on Imaging-based Diagnosis of COVID-19
HIGHLIGHT:      To address this problem, here we propose a patch-based convolutional neural network approach with a relatively small number of trainable parameters for COVID-19 diagnosis.

54, TITLE:      Synchronization of Deterministic Visibly Push-Down Automata
http://arxiv.org/abs/2005.01374
AUTHORS:        Henning Fernau ; Petra Wolf
HIGHLIGHT:      We generalize the concept of synchronizing words for finite automata, which map all states of the automata to the same state, to deterministic visibly push-down automata.

55, TITLE:      DeepEDN: A Deep Learning-based Image Encryption and Decryption Network for Internet of Medical Things
http://arxiv.org/abs/2004.05523
AUTHORS:        Yi Ding ; Guozheng Wu ; Dajiang Chen ; Ning Zhang ; Linpeng Gong ; Mingsheng Cao ; Zhiguang Qin
HIGHLIGHT:      In this work, a deep learning based encryption and decryption network (DeepEDN) is proposed to fulfill the process of encrypting and decrypting the medical image.

56, TITLE:      Bounding the expected run-time of nonconvex optimization with early stopping
http://arxiv.org/abs/2002.08856
AUTHORS:        Thomas Flynn ; Kwang Min Yu ; Abid Malik ; Nicolas D'Imperio ; Shinjae Yoo
HIGHLIGHT:      We develop the approach in the general setting of a first-order optimization algorithm, with possibly biased update directions subject to a geometric drift condition.

57, TITLE:      Image Quality Assessment: Unifying Structure and Texture Similarity
http://arxiv.org/abs/2004.07728
AUTHORS:        Keyan Ding ; Kede Ma ; Shiqi Wang ; Eero P. Simoncelli
HIGHLIGHT:      Here we develop the first full-reference image quality model with explicit tolerance to texture resampling.

58, TITLE:      POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)
http://arxiv.org/abs/2004.12084
AUTHORS:        Jannis Born ; Gabriel Brändle ; Manuel Cossio ; Marion Disdier ; Julie Goulet ; Jérémie Roulin ; Nina Wiedemann
COMMENTS:       7 pages, 4 figures
HIGHLIGHT:      Our contribution is threefold.

59, TITLE:      A Gentzen-style monadic translation of Gödel's System T
http://arxiv.org/abs/1908.05979
AUTHORS:        Chuangjie Xu
COMMENTS:       17 pages. Changes: (1) remove the restriction of satisfying the monad laws in the definition of nuclei, (2) add a unified theorem of logical relation. This paper will appear in FSCD 2020
HIGHLIGHT:      We introduce a syntactic translation of Goedel's System T parametrized by a weak notion of a monad, and prove a corresponding fundamental theorem of logical relation.