2020.05.22.txt

==========New Papers==========
1, TITLE:       Complexity Analysis Of Next-Generation VVC Encoding and Decoding
http://arxiv.org/abs/2005.10801
AUTHORS:        Farhad Pakdaman ; Mohammad Ali Adelimanesh ; Moncef Gabbouj ; Mahmoud Reza Hashemi
COMMENTS:       IEEE ICIP 2020
HIGHLIGHT:      This paper thoroughly analyzes this complexity for both encoder and decoder of VVC Test Model 6, by quantifying the complexity break-down for each coding tool and measuring the complexity and memory requirements for VVC encoding/decoding.

2, TITLE:       Computationally efficient sparse clustering
http://arxiv.org/abs/2005.10817
AUTHORS:        Matthias Löffler ; Alexander S. Wein ; Afonso S. Bandeira
COMMENTS:       26 pages
HIGHLIGHT:      Compared to these existing results, we cover a wider set of parameter regimes and give a more precise understanding of the runtime required and the misclustering error achievable.

3, TITLE:       Instance-aware Image Colorization
http://arxiv.org/abs/2005.10825
AUTHORS:        Jheng-Wei Su ; Hung-Kuo Chu ; Jia-Bin Huang
COMMENTS:       CVPR 2020. Project: https://ericsujw.github.io/InstColorization/ Code: https://github.com/ericsujw/InstColorization
HIGHLIGHT:      In this paper, we propose a method for achieving instance-aware colorization.

4, TITLE:       Hierarchical Multi-Scale Attention for Semantic Segmentation
http://arxiv.org/abs/2005.10821
AUTHORS:        Andrew Tao ; Karan Sapra ; Bryan Catanzaro
COMMENTS:       11 pages, 5 figures
HIGHLIGHT:      In this work, we present an attention-based approach to combining multi-scale predictions.

5, TITLE:       Unsupervised Quality Estimation for Neural Machine Translation
http://arxiv.org/abs/2005.10608
AUTHORS:        Marina Fomicheva ; Shuo Sun ; Lisa Yankovskaya ; Frédéric Blain ; Francisco Guzmán ; Mark Fishel ; Nikolaos Aletras ; Vishrav Chaudhary ; Lucia Specia
COMMENTS:       Accepted for publication in TACL
HIGHLIGHT:      As an alternative, we devise an unsupervised approach to QE where no training or access to additional resources besides the MT system itself is required. To evaluate our approach we collect the first dataset that enables work on both black-box and glass-box approaches to QE.

6, TITLE:       A Neural Network Looks at Leonardo's(?) Salvator Mundi
http://arxiv.org/abs/2005.10600
AUTHORS:        Steven J. Frank ; Andrea M. Frank
COMMENTS:       This is the author's final version. The article has been accepted for publication in Leonardo (MIT Press)
HIGHLIGHT:      We use convolutional neural networks (CNNs) to analyze authorship questions surrounding the works of Leonardo da Vinci -- in particular, Salvator Mundi, the world's most expensive painting and among the most controversial.

7, TITLE:       Efficient and Phase-aware Video Super-resolution for Cardiac MRI
http://arxiv.org/abs/2005.10626
AUTHORS:        Jhih-Yuan Lin ; Yu-Cheng Chang ; Winston H. Hsu
HIGHLIGHT:      To this end, we propose a novel end-to-end trainable network to solve CMR video super-resolution problem without the hardware upgrade and the scanning protocol modifications.

8, TITLE:       Hidden Markov Chains, Entropic Forward-Backward, and Part-Of-Speech Tagging
http://arxiv.org/abs/2005.10629
AUTHORS:        Elie Azeraf ; Emmanuel Monfrini ; Emmanuel Vignon ; Wojciech Pieczynski
COMMENTS:       5 pages, 2 figures, 1 table
HIGHLIGHT:      In this paper, we show that the problem is not due to HMC itself, but to the way its restoration algorithms are computed.

9, TITLE:       SymJAX: symbolic CPU/GPU/TPU programming
http://arxiv.org/abs/2005.10635
AUTHORS:        Randall Balestriero
HIGHLIGHT:      SymJAX: symbolic CPU/GPU/TPU programming

10, TITLE:      Training Keyword Spotting Models on Non-IID Data with Federated Learning
http://arxiv.org/abs/2005.10406
AUTHORS:        Andrew Hard ; Kurt Partridge ; Cameron Nguyen ; Niranjan Subrahmanya ; Aishanee Shah ; Pai Zhu ; Ignacio Lopez Moreno ; Rajiv Mathews
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We demonstrate that a production-quality keyword-spotting model can be trained on-device using federated learning and achieve comparable false accept and false reject rates to a centrally-trained model.

11, TITLE:      RuBQ: A Russian Dataset for Question Answering over Wikidata
http://arxiv.org/abs/2005.10659
AUTHORS:        Vladislav Korablinov ; Pavel Braslavski
HIGHLIGHT:      The paper presents RuBQ, the first Russian knowledge base question answering (KBQA) dataset.

12, TITLE:      Automated Question Answer medical model based on Deep Learning Technology
http://arxiv.org/abs/2005.10416
AUTHORS:        Abdelrahman Abdallah ; Mahmoud Kasem ; Mohamed Hamada ; Shaymaa Sdeek
HIGHLIGHT:      This research will introduce a solution to this problem by automating the process of generating qualified answers to these questions and creating a kind of digital doctor.

13, TITLE:      Interpretable and Accurate Fine-grained Recognition via Region Grouping
http://arxiv.org/abs/2005.10411
AUTHORS:        Zixuan Huang ; Yin Li
COMMENTS:       Accepted to CVPR 2020 (Oral)
HIGHLIGHT:      We present an interpretable deep model for fine-grained visual recognition.

14, TITLE:      Towards Finite-State Morphology of Kurdish
http://arxiv.org/abs/2005.10652
AUTHORS:        Sina Ahmadi ; Hossein Hassani
COMMENTS:       Manuscript submitted to ACM-TALLIP
HIGHLIGHT:      In this paper, as the first attempt of its kind, the morphology of the Kurdish language (Sorani dialect) is described from a computational point of view.

15, TITLE:      Stance Prediction and Claim Verification: An Arabic Perspective
http://arxiv.org/abs/2005.10410
AUTHORS:        Jude Khouja
COMMENTS:       To be presented at FEVER workshop at ACL 2020
HIGHLIGHT:      We describe the methodology for creating the corpus and the annotation process.

16, TITLE:      Wish You Were Here: Context-Aware Human Generation
http://arxiv.org/abs/2005.10663
AUTHORS:        Oran Gafni ; Lior Wolf
HIGHLIGHT:      We present a novel method for inserting objects, specifically humans, into existing images, such that they blend in a photorealistic manner, while respecting the semantic context of the scene.

17, TITLE:      Towards Streaming Image Understanding
http://arxiv.org/abs/2005.10420
AUTHORS:        Mengtian Li ; Yu-Xiong Wang ; Deva Ramanan
COMMENTS:       Project page is at https://www.cs.cmu.edu/~mengtial/proj/streaming/
HIGHLIGHT:      To these ends, we present an approach that coherently integrates latency and accuracy into a single metric for real-time online perception, which we refer to as "streaming accuracy".

18, TITLE:      HF-UNet: Learning Hierarchically Inter-Task Relevance in Multi-Task U-Net for Accurate Prostate Segmentation
http://arxiv.org/abs/2005.10439
AUTHORS:        Kelei He ; Chunfeng Lian ; Bing Zhang ; Xin Zhang ; Xiaohuan Cao ; Dong Nie ; Yang Gao ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      In this paper, we tackle the challenging task of prostate segmentation in CT images by a two-stage network with 1) the first stage to fast localize, and 2) the second stage to accurately segment the prostate.

19, TITLE:      Text-to-Text Pre-Training for Data-to-Text Tasks
http://arxiv.org/abs/2005.10433
AUTHORS:        Mihir Kale
HIGHLIGHT:      We study the pre-train + fine-tune strategy for data-to-text tasks.

20, TITLE:      Gender Slopes: Counterfactual Fairness for Computer Vision Models by Attribute Manipulation
http://arxiv.org/abs/2005.10430
AUTHORS:        Jungseock Joo ; Kimmo Kärkkäinen
HIGHLIGHT:      We propose to use an encoder-decoder network developed for image attribute manipulation to synthesize facial images varying in the dimensions of gender and race while keeping other signals intact.

21, TITLE:      Worse WER, but Better BLEU? Leveraging Word Embedding as Intermediate in Multitask End-to-End Speech Translation
http://arxiv.org/abs/2005.10678
AUTHORS:        Shun-Po Chuang ; Tzu-Wei Sung ; Alexander H. Liu ; Hung-yi Lee
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      Because whether the output of the recognition decoder has the correct semantics is more critical than its accuracy, we propose to improve the multitask ST model by utilizing word embedding as the intermediate.

22, TITLE:      Deep learning-based automated image segmentation for concrete petrographic analysis
http://arxiv.org/abs/2005.10434
AUTHORS:        Yu Song ; Zilong Huang ; Chuanyue Shen ; Honghui Shi ; David A Lange
COMMENTS:       26 pages, 8 figures
HIGHLIGHT:      In this study, we investigated the feasibility of using CNN to conduct concrete segmentation without the use of color treatment.

23, TITLE:      CPOT: Channel Pruning via Optimal Transport
http://arxiv.org/abs/2005.10451
AUTHORS:        Yucong Shen ; Li Shen ; Hao-Zhi Huang ; Xuan Wang ; Wei Liu
COMMENTS:       11 pages
HIGHLIGHT:      To address such a challenging issue, we propose a new technique of Channel Pruning via Optimal Transport, dubbed CPOT.

24, TITLE:      MTSS: Learn from Multiple Domain Teachers and Become a Multi-domain Dialogue Expert
http://arxiv.org/abs/2005.10450
AUTHORS:        Shuke Peng ; Feng Ji ; Zehao Lin ; Shaobo Cui ; Haiqing Chen ; Yin Zhang
COMMENTS:       AAAI 2020, Spotlight Paper
HIGHLIGHT:      In this paper, we propose a novel method to acquire a satisfying policy and subtly circumvent the knotty dialogue state representation problem in the multi-domain setting.

25, TITLE:      MultiMWE: Building a Multi-lingual Multi-Word Expression (MWE) Parallel Corpora
http://arxiv.org/abs/2005.10583
AUTHORS:        Lifeng Han ; Gareth J. F. Jones ; Alan F. Smeaton
COMMENTS:       Accepted to LREC2020
HIGHLIGHT:      In this paper, we present multi-lingual and bilingual MWE corpora that we have extracted from root parallel corpora.

26, TITLE:      MBA-RainGAN: Multi-branch Attention Generative Adversarial Network for Mixture of Rain Removal from Single Images
http://arxiv.org/abs/2005.10582
AUTHORS:        Yiyang Shen ; Yidan Feng ; Sen Deng ; Dong Liang ; Jing Qin ; Haoran Xie ; Mingqiang Wei
HIGHLIGHT:      We observe three intriguing phenomenons that, 1) rain is a mixture of raindrops, rain streaks and rainy haze; 2) the depth from the camera determines the degrees of object visibility, where objects nearby and faraway are visually blocked by rain streaks and rainy haze, respectively; and 3) raindrops on the glass randomly affect the object visibility of the whole image space.

27, TITLE:      ScriptWriter: Narrative-Guided Script Generation
http://arxiv.org/abs/2005.10331
AUTHORS:        Yutao Zhu ; Ruihua Song ; Zhicheng Dou ; Jian-Yun Nie ; Jin Zhou
COMMENTS:       ACL 2020 Camera Ready
HIGHLIGHT:      In this paper, we address a key problem involved in these applications -- guiding a dialogue by a narrative.

28, TITLE:      Adversarial Canonical Correlation Analysis
http://arxiv.org/abs/2005.10349
AUTHORS:        Benjamin Dutton
HIGHLIGHT:      In this work, we explore straightforward adversarial alternatives to recent work in Deep Variational CCA (VCCA and VCCA-Private) we call ACCA and ACCA-Private and show how these approaches offer a stronger and more flexible way to match the approximate posteriors coming from encoders to much larger classes of priors than the VCCA and VCCA-Private models.

29, TITLE:      Bridging the gap between Natural and Medical Images through Deep Colorization
http://arxiv.org/abs/2005.10589
AUTHORS:        Lia Morra ; Luca Piano ; Fabrizio Lamberti ; Tatiana Tommasi
HIGHLIGHT:      In this work, we propose to disentangle those challenges and design a dedicated network module that focuses on color adaptation.

30, TITLE:      VideoForensicsHQ: Detecting High-quality Manipulated Face Videos
http://arxiv.org/abs/2005.10360
AUTHORS:        Gereon Fox ; Wentao Liu ; Hyeongwoo Kim ; Hans-Peter Seidel ; Mohamed Elgharib ; Christian Theobalt
COMMENTS:       21 pages, 9 figures
HIGHLIGHT:      The research community therefore developed techniques for automated detection of modified imagery, and assembled benchmark datasets showing manipulatons by state-of-the-art techniques. In this paper, we contribute to this initiative in two ways: First, we present a new audio-visual benchmark dataset.

31, TITLE:      TAO: A Large-Scale Benchmark for Tracking Any Object
http://arxiv.org/abs/2005.10356
AUTHORS:        Achal Dave ; Tarasha Khurana ; Pavel Tokmakov ; Cordelia Schmid ; Deva Ramanan
COMMENTS:       Project page: http://taodataset.org/
HIGHLIGHT:      To bridge this gap, we introduce a similarly diverse dataset for Tracking Any Object (TAO).

32, TITLE:      WHENet: Real-time Fine-Grained Estimation for Wide Range Head Pose
http://arxiv.org/abs/2005.10353
AUTHORS:        Yijun Zhou ; James Gregson
HIGHLIGHT:      We present an end-to-end head-pose estimation network designed to predict Euler angles through the full range head yaws from a single RGB image.

33, TITLE:      MDPs with Unawareness in Robotics
http://arxiv.org/abs/2005.10381
AUTHORS:        Nan Rong ; Joseph Y. Halpern ; Ashutosh Saxena
COMMENTS:       Appears in Proceedings of the 32nd Conference on Uncertainty in AI (UAI 2016), 2016
HIGHLIGHT:      We formalize decision-making problems in robotics and automated control using continuous MDPs and actions that take place over continuous time intervals.

34, TITLE:      Information Acquisition Under Resource Limitations in a Noisy Environment
http://arxiv.org/abs/2005.10383
AUTHORS:        Matvey Soloviev ; Joseph Y. Halpern
COMMENTS:       A preliminary version of the paper appeared in \emph{Proceedings of the Thirty-Second AAAI Conference on Artificial Intelligence (AAAI-18)}, 2018
HIGHLIGHT:      We introduce a theoretical model of information acquisition under resource limitations in a noisy environment.

35, TITLE:      Formal Specification and Verification of Solidity Contracts with Events
http://arxiv.org/abs/2005.10382
AUTHORS:        Ákos Hajdu ; Dejan Jovanović ; Gabriela Ciocarlie
HIGHLIGHT:      This paper presents a source-level approach for the formal specification and verification of Solidity contracts with the primary focus on events.

36, TITLE:      Investigation of learning abilities on linguistic features in sequence-to-sequence text-to-speech synthesis
http://arxiv.org/abs/2005.10390
AUTHORS:        Yusuke Yasuda ; Xin Wang ; Junichi Yamagishi
HIGHLIGHT:      In this paper we investigate under what conditions the neural sequence-to-sequence TTS can work well in Japanese and English along with comparisons with deep neural network (DNN) based pipeline TTS systems.

37, TITLE:      Pretraining with Contrastive Sentence Objectives Improves Discourse Performance of Language Models
http://arxiv.org/abs/2005.10389
AUTHORS:        Dan Iter ; Kelvin Guu ; Larry Lansing ; Dan Jurafsky
COMMENTS:       AC2020
HIGHLIGHT:      We propose CONPONO, an inter-sentence objective for pretraining language models that models discourse coherence and the distance between sentences.

38, TITLE:      Beyond User Self-Reported Likert Scale Ratings: A Comparison Model for Automatic Dialog Evaluation
http://arxiv.org/abs/2005.10716
AUTHORS:        Weixin Liang ; James Zou ; Zhou Yu
HIGHLIGHT:      To alleviate this problem, we formulate dialog evaluation as a comparison task.

39, TITLE:      Multi-agent model for risk prediction in surgery
http://arxiv.org/abs/2005.10738
AUTHORS:        Bruno Perez ; Julien Henriet ; Christophe Lang ; Laurent Philippe
HIGHLIGHT:      This article presents our model, its implementation and the first results obtained.

40, TITLE:      Distributed Verifiers in PCP
http://arxiv.org/abs/2005.10749
AUTHORS:        Nagaganesh Jaladanki ; Wilson Wu
COMMENTS:       6 pages, 0 figures
HIGHLIGHT:      In this paper, we will first go over the past work in the $\mathsf{LCP}$ and $\mathsf{dIP}$ space before showing properties and proofs in our $\mathsf{dPCP}$ system.

41, TITLE:      Hardness of Modern Games
http://arxiv.org/abs/2005.10506
AUTHORS:        Diogo M. Costa ; Alexandre P. Francisco ; Luís M. S. Russo
COMMENTS:       The work reported in this article was supported by national funds through Funda\c{c}\~ao para a Ci\^encia e Tecnologia (FCT) through projects NGPHYLO PTDC/CCI-BIO/29676/2017 and UID/CEC/50021/2019. Funded in part by European Union Horizon 2020 research and innovation programme under the Marie Sk{\l}odowska-Curie Actions grant agreement No 690941
HIGHLIGHT:      We consider the complexity properties of modern puzzle games, Hexiom, Cut the Rope and Back to Bed.

42, TITLE:      A Nearest Neighbor Network to Extract Digital Terrain Models from 3D Point Clouds
http://arxiv.org/abs/2005.10745
AUTHORS:        Mohammed Yousefhussiena ; David J. Kelbeb ; Carl Salvaggioa
COMMENTS:       Preprint submitted to Science of Remote Sensing
HIGHLIGHT:      In contrast, here we present an algorithm that directly operates on 3D-point clouds and estimate the underlying DTM for the scene using an end-to-end approach without the need to classify points into ground and non-ground cover types.

43, TITLE:      Tensor Clustering with Planted Structures: Statistical Optimality and Computational Limits
http://arxiv.org/abs/2005.10743
AUTHORS:        Yuetian Luo ; Anru R. Zhang
HIGHLIGHT:      We focus on two clustering models, constant high-order clustering (CHC) and rank-one higher-order clustering (ROHC), and study the methods and theories for testing whether a cluster exists (detection) and identifying the support of cluster (recovery).

44, TITLE:      Robust Ensemble Model Training via Random Layer Sampling Against Adversarial Attack
http://arxiv.org/abs/2005.10757
AUTHORS:        Hakmin Lee ; Hong Joo Lee ; Seong Tae Kim ; Yong Man Ro
HIGHLIGHT:      In this paper, we propose an ensemble model training framework with random layer sampling to improve the robustness of deep neural networks.

45, TITLE:      Few-shot Compositional Font Generation with Dual Memory
http://arxiv.org/abs/2005.10510
AUTHORS:        Junbum Cha ; Sanghyuk Chun ; Gayoung Lee ; Bado Lee ; Seonghyeon Kim ; Hwalsuk Lee
HIGHLIGHT:      In this paper, we focus on compositional scripts, a widely used letter system in the world, where each glyph can be decomposed by several components.

46, TITLE:      Revisiting Role of Autoencoders in Adversarial Settings
http://arxiv.org/abs/2005.10750
AUTHORS:        Byeong Cheon Kim ; Jung Uk Kim ; Hakmin Lee ; Yong Man Ro
COMMENTS:       Accepted at ICIP 2020
HIGHLIGHT:      In this paper, we revisit the role of autoencoders in adversarial settings.

47, TITLE:      Unsupervised segmentation via semantic-apparent feature fusion
http://arxiv.org/abs/2005.10513
AUTHORS:        Xi Li ; Huimin Ma ; Hongbing Ma ; Yidong Wang
COMMENTS:       in Chinese. Accepted by NCIG 2020
HIGHLIGHT:      In order to solve this problem, the research proposes an unsupervised foreground segmentation method based on semantic-apparent feature fusion (SAFF).

48, TITLE:      Efficient Ensemble Model Generation for Uncertainty Estimation with Bayesian Approximation in Segmentation
http://arxiv.org/abs/2005.10754
AUTHORS:        Hong Joo Lee ; Seong Tae Kim ; Nassir Navab ; Yong Man Ro
HIGHLIGHT:      To address this issue, a generic and efficient segmentation framework to construct ensemble segmentation models is devised in this paper.

49, TITLE:      Powering One-shot Topological NAS with Stabilized Share-parameter Proxy
http://arxiv.org/abs/2005.10511
AUTHORS:        Ronghao Guo ; Chen Lin ; Chuming Li ; Keyu Tian ; Ming Sun ; Lu Sheng ; Junjie Yan
HIGHLIGHT:      In this work, we try to enhance the one-shot NAS by exploring high-performing network architectures in our large-scale Topology Augmented Search Space (i.e., over 3.4*10^10 different topological structures).

50, TITLE:      LaCulturaNonSiFerma -- Report su uso e la diffusione degli hashtag delle istituzioni culturali italiane durante il periodo di lockdown
http://arxiv.org/abs/2005.10527
AUTHORS:        Carola Carlino ; Gennaro Nolano ; Maria Pia di Buono ; Johanna Monti
COMMENTS:       in Italian
HIGHLIGHT:      This report presents an analysis of #hashtags used by Italian Cultural Heritage institutions to promote and communicate cultural content during the COVID-19 lock-down period in Italy.

51, TITLE:      Dense Semantic 3D Map Based Long-Term Visual Localization with Hybrid Features
http://arxiv.org/abs/2005.10766
AUTHORS:        Tianxin Shi ; Hainan Cui ; Zhuo Song ; Shuhan Shen
HIGHLIGHT:      In this paper, we present a novel visual localization method using hybrid handcrafted and learned features with dense semantic 3D map.

52, TITLE:      HyperSTAR: Task-Aware Hyperparameters for Deep Networks
http://arxiv.org/abs/2005.10524
AUTHORS:        Gaurav Mittal ; Chang Liu ; Nikolaos Karianakis ; Victor Fragoso ; Mei Chen ; Yun Fu
COMMENTS:       Published at CVPR 2020 (Oral)
HIGHLIGHT:      To reduce HPO time, we present HyperSTAR (System for Task Aware Hyperparameter Recommendation), a task-aware method to warm-start HPO for deep neural networks.

53, TITLE:      An approach to Beethoven's 10th Symphony
http://arxiv.org/abs/2005.10539
AUTHORS:        Paula Muñoz-Lago ; Gonzalo Méndez
HIGHLIGHT:      As we dispose of a great amount of data belonging to his work, the purpose of this paper is to investigate the possibility of extracting patterns on his compositional model from symbolic data and generate what would have been his last symphony, the Tenth.

54, TITLE:      Manifold Alignment for Semantically Aligned Style Transfer
http://arxiv.org/abs/2005.10777
AUTHORS:        Jing Huo ; Shiyin Jin ; Wenbin Li ; Jing Wu ; Yu-Kun Lai ; Yinghuan Shi ; Yang Gao
COMMENTS:       10 pages
HIGHLIGHT:      In this paper, we make a different assumption that local semantically aligned (or similar) regions between the content and style images should share similar style patterns.

55, TITLE:      Region Proposals for Saliency Map Refinement for Weakly-supervised Disease Localisation and Classification
http://arxiv.org/abs/2005.10550
AUTHORS:        Renato Hermoza ; Gabriel Maicas ; Jacinto C. Nascimento ; Gustavo Carneiro
COMMENTS:       Early accept at MICCAI 2020
HIGHLIGHT:      In this paper, we introduce a new model that combines region proposal and saliency detection to overcome both limitations for weakly supervised disease classification and localisation.

56, TITLE:      The Frankfurt Latin Lexicon: From Morphological Expansion and Word Embeddings to SemioGraphs
http://arxiv.org/abs/2005.10790
AUTHORS:        Alexander Mehler ; Bernhard Jussen ; Tim Geelhaar ; Alexander Henlein ; Giuseppe Abrami ; Daniel Baumartz ; Tolga Uslu ; Wahed Hemati
COMMENTS:       22 pages, 7 figures, 5 tabels
HIGHLIGHT:      In this article we present the Frankfurt Latin Lexicon (FLL), a lexical resource for Medieval Latin that is used both for the lemmatization of Latin texts and for the post-editing of lemmatizations.

57, TITLE:      Omnidirectional Images as Moving Camera Videos
http://arxiv.org/abs/2005.10547
AUTHORS:        Xiangjie Sui ; Kede Ma ; Yiru Yao ; Yuming Fang
COMMENTS:       Code is available, see https://github.com/xiangjieSui/Omnidirectional-Images-as-Moving-Camera-Videos
HIGHLIGHT:      In this paper, we propose a principled computational framework for objective quality assessment of 360{\deg} images, which embodies the threefold behavior in a delightful way. We construct a set of specific quality measures within the proposed framework, and demonstrate their promises on two VR quality databases.

58, TITLE:      Cross-Domain Few-Shot Learning with Meta Fine-Tuning
http://arxiv.org/abs/2005.10544
AUTHORS:        John Cai ; Sheng Mei Shen
COMMENTS:       3 Tables, 3 Figures
HIGHLIGHT:      In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark proposed by the CVPR 2020 Challenge.

59, TITLE:      SafeComp: Protocol For Certifying Cloud Computations Integrity
http://arxiv.org/abs/2005.10786
AUTHORS:        Evgeny Shishkin ; Evgeny Kislitsyn
HIGHLIGHT:      We present a multi-party interactive protocol called SafeComp that solves this problem under specified constraints.

60, TITLE:      Repairing and Mechanising the JavaScript Relaxed Memory Model
http://arxiv.org/abs/2005.10554
AUTHORS:        Conrad Watt ; Christopher Pulte ; Anton Podkopaev ; Guillaume Barbier ; Stephen Dolan ; Shaked Flur ; Jean Pichon-Pharabod ; Shu-yu Guo
COMMENTS:       16 pages, 13 figiures
HIGHLIGHT:      We propose a correction, which also incorporates a previously proposed fix for a failure of the model to provide Sequential Consistency of Data-Race-Free programs (SC-DRF), an important correctness condition.

61, TITLE:      Maplets: An Efficient Approach for Cooperative SLAM Map Building Under Communication and Computation Constraints
http://arxiv.org/abs/2005.10310
AUTHORS:        Kevin M. Brink ; Jincheng Zhang ; Andrew R. Willis ; Ryan E. Sherrill ; Jamie L. Godwin
HIGHLIGHT:      This article introduces an approach to facilitate cooperative exploration and mapping of large-scale, near-ground, underground, or indoor spaces via a novel integration framework for locally-dense agent map data.

62, TITLE:      InfoScrub: Towards Attribute Privacy by Targeted Obfuscation
http://arxiv.org/abs/2005.10329
AUTHORS:        Hui-Po Wang ; Tribhuvanesh Orekondy ; Mario Fritz
COMMENTS:       20 pages, 7 figures
HIGHLIGHT:      We tackle this problem in a novel image obfuscation framework: to maximize entropy on inferences over targeted privacy attributes, while retaining image fidelity.

63, TITLE:      A Unifying Model for Locally Constrained Spanning Tree Problems
http://arxiv.org/abs/2005.10328
AUTHORS:        Luiz Alberto do Carmo Viana ; Manoel Campêlo ; Ignasi Sau ; Ana Silva
COMMENTS:       28 pages, 6 figures
HIGHLIGHT:      Given a graph $G$ and a digraph $D$ whose vertices are the edges of $G$, we investigate the problem of finding a spanning tree of $G$ that satisfies the constraints imposed by $D$.

64, TITLE:      A quantum procedure for map generation
http://arxiv.org/abs/2005.10327
AUTHORS:        James R. Wootton
COMMENTS:       To be published in the proceedings of the IEEE Conference on Games
HIGHLIGHT:      In this paper, we begin to explore whether near-term quantum computers could provide tools that are useful in the creation and implementation of computer games.

65, TITLE:      A Study of Deep Learning Colon Cancer Detection in Limited Data Access Scenarios
http://arxiv.org/abs/2005.10326
AUTHORS:        Apostolia Tsirikoglou ; Karin Stacke ; Gabriel Eilertsen ; Martin Lindvall ; Jonas Unger
COMMENTS:       Presented at the ICLR 2020 Workshop on AI for Overcoming Global Disparities in Cancer Care (AI4CC)
HIGHLIGHT:      In this study, we investigate mitigation strategies for limited data access scenarios.

66, TITLE:      Single Image Super-Resolution via Residual Neuron Attention Networks
http://arxiv.org/abs/2005.10455
AUTHORS:        Wenjie Ai ; Xiaoguang Tu ; Shilei Cheng ; Mei Xie
COMMENTS:       6 pages, 4 figures, Accepted by IEEE ICIP 2020
HIGHLIGHT:      In this paper, we propose a novel end-to-end Residual Neuron Attention Networks (RNAN) for more efficient and effective SISR.

67, TITLE:      Symptom extraction from the narratives of personal experiences with COVID-19 on Reddit
http://arxiv.org/abs/2005.10454
AUTHORS:        Curtis Murray ; Lewis Mitchell ; Jonathan Tuke ; Mark Mackay
HIGHLIGHT:      We identified two clear clusters of positive and negative emotions associated with the evolution of these symptoms and mapped their relationships.

68, TITLE:      Novel Policy Seeking with Constrained Optimization
http://arxiv.org/abs/2005.10696
AUTHORS:        Hao Sun ; Zhenghao Peng ; Bo Dai ; Jian Guo ; Dahua Lin ; Bolei Zhou
HIGHLIGHT:      In this work, we address the problem of learning to seek novel policies in reinforcement learning tasks.

69, TITLE:      Multistream CNN for Robust Acoustic Modeling
http://arxiv.org/abs/2005.10470
AUTHORS:        Kyu J. Han ; Jing Pan ; Venkata Krishna Naveen Tadala ; Tao Ma ; Dan Povey
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      This paper presents multistream CNN, a novel neural network architecture for robust acoustic modeling in speech recognition tasks.

70, TITLE:      Fluent Response Generation for Conversational Question Answering
http://arxiv.org/abs/2005.10464
AUTHORS:        Ashutosh Baheti ; Alan Ritter ; Kevin Small
COMMENTS:       2020 Annual Conference of the Association for Computational Linguistics
HIGHLIGHT:      In this work, we propose a method for situating QA responses within a SEQ2SEQ NLG approach to generate fluent grammatical answer responses while maintaining correctness.

71, TITLE:      Simplified Self-Attention for Transformer-based End-to-End Speech Recognition
http://arxiv.org/abs/2005.10463
AUTHORS:        Haoneng Luo ; Shiliang Zhang ; Ming Lei ; Lei Xie
COMMENTS:       Submitted to Interspeech2020
HIGHLIGHT:      In this paper, to reduce the model complexity while maintaining good performance, we propose a simplified self-attention (SSAN) layer which employs FSMN memory block instead of projection layers to form query and key vectors for transformer-based end-to-end speech recognition.

72, TITLE:      ASAPP-ASR: Multistream CNN and Self-Attentive SRU for SOTA Speech Recognition
http://arxiv.org/abs/2005.10469
AUTHORS:        Jing Pan ; Joshua Shapiro ; Jeremy Wohlwend ; Kyu J. Han ; Tao Lei ; Tao Ma
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper we present state-of-the-art (SOTA) performance on the LibriSpeech corpus with two novel neural network architectures, a multistream CNN for acoustic modeling and a self-attentive simple recurrent unit (SRU) for language modeling.

73, TITLE:      AOWS: Adaptive and optimal network width search with latency constraints
http://arxiv.org/abs/2005.10481
AUTHORS:        Maxim Berman ; Leonid Pishchulin ; Ning Xu ; Matthew B. Blaschko ; Gerard Medioni
COMMENTS:       Accepted to CVPR 2020 (oral)
HIGHLIGHT:      We introduce a novel efficient one-shot NAS approach to optimally search for channel numbers, given latency constraints on a specific hardware.

74, TITLE:      Panoptic Instance Segmentation on Pigs
http://arxiv.org/abs/2005.10499
AUTHORS:        Johannes Brünger ; Maria Gentz ; Imke Traulsen ; Reinhard Koch
COMMENTS:       18 pages, 10 figures. Submitted to MDPI Sensors
HIGHLIGHT:      Therefore this work follows the relatively new definition of a panoptic segmentation and aims at the pixel accurate segmentation of the individual pigs.

75, TITLE:      GroupFace: Learning Latent Groups and Constructing Group-based Representations for Face Recognition
http://arxiv.org/abs/2005.10497
AUTHORS:        Yonghyun Kim ; Wonpyo Park ; Myung-Cheol Roh ; Jongju Shin
HIGHLIGHT:      We propose a novel face-recognition-specialized architecture called GroupFace that utilizes multiple group-aware representations, simultaneously, to improve the quality of the embedding feature.

76, TITLE:      Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation
http://arxiv.org/abs/2005.10266
AUTHORS:        Liang-Chieh Chen ; Raphael Gontijo Lopes ; Bowen Cheng ; Maxwell D. Collins ; Ekin D. Cubuk ; Barret Zoph ; Hartwig Adam ; Jonathon Shlens
COMMENTS:       21 pages including reference
HIGHLIGHT:      In this work, we ask if we may leverage semi-supervised learning in unlabeled video sequences to improve the performance on urban scene segmentation, simultaneously tackling semantic, instance, and panoptic segmentation.

77, TITLE:      An Adversarial Approach for Explaining the Predictions of Deep Neural Networks
http://arxiv.org/abs/2005.10284
AUTHORS:        Arash Rahnama ; Andrew Tseng
HIGHLIGHT:      In this work, we present a novel algorithm for explaining the predictions of a DNN using adversarial machine learning.

78, TITLE:      Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation
http://arxiv.org/abs/2005.10283
AUTHORS:        Bryan Eikema ; Wilker Aziz
HIGHLIGHT:      In this work, we criticise NMT models probabilistically showing that stochastic samples following the model's own generative story do reproduce various statistics of the training data well, but that it is beam search that strays from such statistics.

79, TITLE:      Causality, Responsibility and Blame in Team Plans
http://arxiv.org/abs/2005.10297
AUTHORS:        Natasha Alechina ; Joseph Y. Halpern ; Brian Logan
COMMENTS:       {\em Proceedings of the Sixteenth Appears in \emph{Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2017)}, 2017
HIGHLIGHT:      Many objectives can be achieved (or may be achieved more effectively) only by a group of agents executing a team plan.



==========Updates to Previous Papers==========
1, TITLE:       Transformer Based Language Models for Similar Text Retrieval and Ranking
http://arxiv.org/abs/2005.04588
AUTHORS:        Javed Qadrud-Din ; Ashraf Bah Rabiou ; Ryan Walker ; Ravi Soni ; Martin Gajek ; Gabriel Pack ; Akhil Rangaraj
COMMENTS:       5 pages, 2 figures
HIGHLIGHT:      In this paper, we introduce novel approaches for effectively applying neural transformer models to similar text retrieval and ranking without an initial bag-of-words-based step.

2, TITLE:       SentiBERT: A Transferable Transformer-Based Architecture for Compositional Sentiment Semantics
http://arxiv.org/abs/2005.04114
AUTHORS:        Da Yin ; Tao Meng ; Kai-Wei Chang
COMMENTS:       ACL-2020
HIGHLIGHT:      We propose SentiBERT, a variant of BERT that effectively captures compositional sentiment semantics.

3, TITLE:       Data Augmentation Imbalance For Imbalanced Attribute Classification
http://arxiv.org/abs/2004.13628
AUTHORS:        Yang Hu ; Xiaying Bai ; Pan Zhou ; Fanhua Shang ; Shengmei Shen
COMMENTS:       This paper needs further revision
HIGHLIGHT:      In this paper, we propose a new re-sampling algorithm called: data augmentation imbalance (DAI) to explicitly enhance the ability to discriminate the fewer attributes via increasing the proportion of labels accounting for a small part.

4, TITLE:       Automated Lane Change Strategy using Proximal Policy Optimization-based Deep Reinforcement Learning
http://arxiv.org/abs/2002.02667
AUTHORS:        Fei Ye ; Xuxin Cheng ; Pin Wang ; Ching-Yao Chan ; Jiucai Zhang
HIGHLIGHT:      In this study, we propose an automated lane change strategy using proximal policy optimization-based deep reinforcement learning, which shows great advantages in learning efficiency while still maintaining stable performance.

5, TITLE:       Robust Screening of COVID-19 from Chest X-ray via Discriminative Cost-Sensitive Learning
http://arxiv.org/abs/2004.12592
AUTHORS:        Tianyang Li ; Zhongyi Han ; Benzheng Wei ; Yuanjie Zheng ; Yanfei Hong ; Jinyu Cong
COMMENTS:       Under review
HIGHLIGHT:      In this paper, we report our solution, discriminative cost-sensitive learning (DCSL), which should be the choice if the clinical needs the assisted screening of COVID-19 from chest X-rays. We collected a large-scale multi-class dataset comprised of 2,239 chest X-ray examples: 239 examples from confirmed COVID-19 cases, 1,000 examples with confirmed bacterial or viral pneumonia cases, and 1,000 examples of healthy people.

6, TITLE:       Attention-based network for low-light image enhancement
http://arxiv.org/abs/2005.09829
AUTHORS:        Cheng Zhang ; Qingsen Yan ; Yu zhu ; Xianjun Li ; Jinqiu Sun ; Yanning Zhang
HIGHLIGHT:      To address such a difficult problem, this paper presents a novel attention-based neural network to generate high-quality enhanced low-light images from the raw sensor data.

7, TITLE:       A Unified MRC Framework for Named Entity Recognition
http://arxiv.org/abs/1910.11476
AUTHORS:        Xiaoya Li ; Jingrong Feng ; Yuxian Meng ; Qinghong Han ; Fei Wu ; Jiwei Li
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we propose a unified framework that is capable of handling both flat and nested NER tasks.

8, TITLE:       Settling the Complexity of Arrow-Debreu Markets under Leontief and PLC Utilities, using the Classes FIXP and \Exists-R
http://arxiv.org/abs/1411.5060
AUTHORS:        Jugal Garg ; Ruta Mehta ; Vijay V. Vazirani ; Sadra Yazdanbod
HIGHLIGHT:      This paper resolves two of the handful of remaining questions on the computability of market equilibria, a central theme within algorithmic game theory (AGT).

9, TITLE:       Learning Task-Oriented Grasping from Human Activity Datasets
http://arxiv.org/abs/1910.11669
AUTHORS:        Mia Kokic ; Danica Kragic ; Jeannette Bohg
HIGHLIGHT:      We propose to leverage a real-world, human activity RGB dataset to teach a robot Task-Oriented Grasping (TOG).

10, TITLE:      Input Dropout for Spatially Aligned Modalities
http://arxiv.org/abs/2002.02852
AUTHORS:        Sébastien de Blois ; Mathieu Garon ; Christian Gagné ; Jean-François Lalonde
COMMENTS:       Accepted in ICIP 2020. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
HIGHLIGHT:      By assuming that the modalities have a strong spatial correlation, we propose Input Dropout, a simple technique that consists in stochastic hiding of one or many input modalities at training time, while using only the canonical (e.g. RGB) modalities at test time.

11, TITLE:      TripletUNet: Multi-Task U-Net with Online Voxel-Wise Learning for Precise CT Prostate Segmentation
http://arxiv.org/abs/2005.07462
AUTHORS:        Kelei He ; Chunfeng Lian ; Ehsan Adeli ; Jing Huo ; Yinghuan Shi ; Yang Gao ; Bing Zhang ; Junfeng Zhang ; Dinggang Shen
HIGHLIGHT:      To address this problem, we propose a two-stage framework.

12, TITLE:      3FabRec: Fast Few-shot Face alignment by Reconstruction
http://arxiv.org/abs/1911.10448
AUTHORS:        Bjoern Browatzki ; Christian Wallraven
HIGHLIGHT:      We introduce a semi-supervised method in which the crucial idea is to first generate implicit face knowledge from the large amounts of unlabeled images of faces available today.

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

14, TITLE:      Neural Lyapunov Control
http://arxiv.org/abs/2005.00611
AUTHORS:        Ya-Chien Chang ; Nima Roohi ; Sicun Gao
COMMENTS:       NeurIPS 2019
HIGHLIGHT:      We propose new methods for learning control policies and neural network Lyapunov functions for nonlinear control problems, with provable guarantee of stability.

15, TITLE:      Graph Neural Networks Meet Neural-Symbolic Computing: A Survey and Perspective
http://arxiv.org/abs/2003.00330
AUTHORS:        Luis C. Lamb ; Artur Garcez ; Marco Gori ; Marcelo Prates ; Pedro Avelar ; Moshe Vardi
COMMENTS:       Updated version, draft of accepted IJCAI2020 Survey Paper
HIGHLIGHT:      In this paper, we review the state-of-the-art on the use of GNNs as a model of neural-symbolic computing.

16, TITLE:      Glyce: Glyph-vectors for Chinese Character Representations
http://arxiv.org/abs/1901.10125
AUTHORS:        Yuxian Meng ; Wei Wu ; Fei Wang ; Xiaoya Li ; Ping Nie ; Fan Yin ; Muyu Li ; Qinghong Han ; Xiaofei Sun ; Jiwei Li
COMMENTS:       Accepted by NeurIPS 2019
HIGHLIGHT:      In this paper, we address this gap by presenting Glyce, the glyph-vectors for Chinese character representations.

17, TITLE:      Synaptic Plasticity Dynamics for Deep Continuous Local Learning (DECOLLE)
http://arxiv.org/abs/1811.10766
AUTHORS:        Jacques Kaiser ; Hesham Mostafa ; Emre Neftci
COMMENTS:       Published in Frontiers in Neuroscience - Neuromorphic Engineering
HIGHLIGHT:      Here, we show that synthetic gradients enable the derivation of Deep Continuous Local Learning (DECOLLE) in spiking neural networks.

18, TITLE:      A clustering-based reinforcement learning approach for tailored personalization of e-Health interventions
http://arxiv.org/abs/1804.03592
AUTHORS:        Ali el Hassouni ; Mark Hoogendoorn ; Martijn van Otterlo ; A. E. Eiben ; Vesa Muhonen ; Eduardo Barbaro
HIGHLIGHT:      We present an approach that learns tailored personalization policies for groups of users by combining RL and clustering.

19, TITLE:      CAGFuzz: Coverage-Guided Adversarial Generative Fuzzing Testing of Deep Learning Systems
http://arxiv.org/abs/1911.07931
AUTHORS:        Pengcheng Zhang ; Qiyin Dai ; Patrizio Pelliccione
HIGHLIGHT:      To address these two problems, in this paper, we propose CAGFuzz, a Coverage-guided Adversarial Generative Fuzzing testing approach, which generates adversarial examples for a targeted DNN to discover its potential defects.

20, TITLE:      Object Pose Estimation in Robotics Revisited
http://arxiv.org/abs/1906.02783
AUTHORS:        Antti Hietanen ; Jyrki Latokartano ; Alessandro Foi ; Roel Pieters ; Ville Kyrki ; Minna Lanz ; Joni-Kristian Kämäräinen
COMMENTS:       29 pages, 8 figures
HIGHLIGHT:      In this work we propose a probabilistic metric that directly measures success in robotic tasks.

21, TITLE:      Synthetic Error Dataset Generation Mimicking Bengali Writing Pattern
http://arxiv.org/abs/2003.03484
AUTHORS:        Md. Habibur Rahman Sifat ; Chowdhury Rafeed Rahman ; Mohammad Rafsan ; Md. Hasibur Rahman
HIGHLIGHT:      In this research, We present an algorithm for automatic misspelled Bengali word generation from correct word through analyzing Bengali writing pattern using QWERTY layout English keyboard.

22, TITLE:      Learning Temporal Coherence via Self-Supervision for GAN-based Video Generation
http://arxiv.org/abs/1811.09393
AUTHORS:        Mengyu Chu ; You Xie ; Jonas Mayer ; Laura Leal-Taixé ; Nils Thuerey
COMMENTS:       Project page: https://ge.in.tum.de/publications/2019-tecogan-chu/, code link: https://github.com/thunil/TecoGAN
HIGHLIGHT:      In contrast, we focus on improving learning objectives and propose a temporally self-supervised algorithm. Additionally, we propose a first set of metrics to quantitatively evaluate the accuracy as well as the perceptual quality of the temporal evolution.

23, TITLE:      KoGuN: Accelerating Deep Reinforcement Learning via Integrating Human Suboptimal Knowledge
http://arxiv.org/abs/2002.07418
AUTHORS:        Peng Zhang ; Jianye Hao ; Weixun Wang ; Hongyao Tang ; Yi Ma ; Yihai Duan ; Yan Zheng
HIGHLIGHT:      Taking this inspiration, we propose knowledge guided policy network (KoGuN), a novel framework that combines human prior suboptimal knowledge with reinforcement learning.

24, TITLE:      Cross-Linguistic Syntactic Evaluation of Word Prediction Models
http://arxiv.org/abs/2005.00187
AUTHORS:        Aaron Mueller ; Garrett Nicolai ; Panayiota Petrou-Zeniou ; Natalia Talmina ; Tal Linzen
COMMENTS:       Accepted for presentation at ACL 2020
HIGHLIGHT:      To investigate how these models' ability to learn syntax varies by language, we introduce CLAMS (Cross-Linguistic Assessment of Models on Syntax), a syntactic evaluation suite for monolingual and multilingual models.

25, TITLE:      Editing in Style: Uncovering the Local Semantics of GANs
http://arxiv.org/abs/2004.14367
AUTHORS:        Edo Collins ; Raja Bala ; Bob Price ; Sabine Süsstrunk
COMMENTS:       IEEE Conference on Computer Vision and Patten Recognition (CVPR), 2020. Code: https://github.com/IVRL/GANLocalEditing
HIGHLIGHT:      Focusing on StyleGAN, we introduce a simple and effective method for making local, semantically-aware edits to a target output image.

26, TITLE:      Long-tail Visual Relationship Recognition with a Visiolinguistic Hubless Loss
http://arxiv.org/abs/2004.00436
AUTHORS:        Sherif Abdelkarim ; Panos Achlioptas ; Jiaji Huang ; Boyang Li ; Kenneth Church ; Mohamed Elhoseiny
HIGHLIGHT:      In this paper, we propose to study a novel task concerning the generalization of visual relationships that are on the distribution's tail, i.e. we investigate how to help AI systems to better recognize rare relationships like <S:dog, P:riding, O:horse>, where the subject S, predicate P, and/or the object O come from the tail of the corresponding distributions. To achieve this goal, we first introduce two large-scale visual-relationship detection benchmarks built upon the widely used Visual Genome and GQA datasets.

27, TITLE:      k-medoids and p-median clustering are solvable in polynomial time for a 2d Pareto front
http://arxiv.org/abs/1806.02098
AUTHORS:        Nicolas Dupin ; Frank Nielsen ; El-Ghazali Talbi
HIGHLIGHT:      This paper examines a common extension of k-medoids and k-median clustering in the case of a two-dimensional Pareto front, as generated by bi-objective optimization approaches.

28, TITLE:      Textual Membership Queries
http://arxiv.org/abs/1805.04609
AUTHORS:        Jonathan Zarecki ; Shaul Markovitch
COMMENTS:       Accepted to IJCAI 2020. Additional material is available at tinyurl.com/sup-textualmqs
HIGHLIGHT:      In order to minimize human labeling efforts, we propose a novel active learning solution that does not rely on existing sources of unlabeled data.

29, TITLE:      On Dimensional Linguistic Properties of the Word Embedding Space
http://arxiv.org/abs/1910.02211
AUTHORS:        Vikas Raunak ; Vaibhav Kumar ; Vivek Gupta ; Florian Metze
COMMENTS:       Published at ACL RepL4NLP 2020
HIGHLIGHT:      In this work, we analyze word embeddings in terms of their principal components and arrive at a number of novel and counterintuitive observations.

30, TITLE:      Semantic Search of Memes on Twitter
http://arxiv.org/abs/2002.01462
AUTHORS:        Jesus Perez-Martin ; Benjamin Bustos ; Magdalena Saldana
COMMENTS:       Computational Methods Interest Group of the 70th International Communication Association Conference, May 2020 Virtual conference presentation link: https://player.vimeo.com/video/418320378
HIGHLIGHT:      This paper proposes and compares several methods for automatically classifying images as memes.

31, TITLE:      Text Matters but Speech Influences: A Computational Analysis of Syntactic Ambiguity Resolution
http://arxiv.org/abs/1910.09275
AUTHORS:        Won Ik Cho ; Jeonghwa Cho ; Woo Hyun Kang ; Nam Soo Kim
COMMENTS:       CogSci 2020 Camera-ready
HIGHLIGHT:      Utilizing a speech corpus recorded on Korean scripts of syntactically ambiguous utterances, we revealed that co-attention frameworks, namely multi-hop attention and cross-attention, show significantly superior performance in disambiguating speech intention.

32, TITLE:      6-DOF Grasping for Target-driven Object Manipulation in Clutter
http://arxiv.org/abs/1912.03628
AUTHORS:        Adithyavairavan Murali ; Arsalan Mousavian ; Clemens Eppner ; Chris Paxton ; Dieter Fox
COMMENTS:       Accepted to the International Conference on Robotics and Automation (ICRA) 2020
HIGHLIGHT:      We present a method that plans 6-DOF grasps for any desired object in a cluttered scene from partial point cloud observations.

33, TITLE:      Training a code-switching language model with monolingual data
http://arxiv.org/abs/1911.06003
AUTHORS:        Shun-Po Chuang ; Tzu-Wei Sung ; Hung-Yi Lee
COMMENTS:       Accepted as an oral presentation in ICASSP 2020
HIGHLIGHT:      We propose an approach to train such CS language models on monolingual data only.

34, TITLE:      Multi-objective multi-generation Gaussian process optimizer for design optimization
http://arxiv.org/abs/1907.00250
AUTHORS:        Xiaobiao Huang ; Minghao Song ; Zhe Zhang
HIGHLIGHT:      We present a multi-objective evolutionary optimization algorithm that uses Gaussian process (GP) regression-based models to select trial solutions in a multi-generation iterative procedure.

35, TITLE:      On Evaluating Weakly Supervised Action Segmentation Methods
http://arxiv.org/abs/2005.09743
AUTHORS:        Yaser Souri ; Alexander Richard ; Luca Minciullo ; Juergen Gall
COMMENTS:       Technical Report
HIGHLIGHT:      In this work, we focus on two aspects of the use and evaluation of weakly supervised action segmentation approaches that are often overlooked: the performance variance over multiple training runs and the impact of selecting feature extractors for this task.

36, TITLE:      RNNFast: An Accelerator for Recurrent Neural Networks Using Domain Wall Memory
http://arxiv.org/abs/1812.07609
AUTHORS:        Mohammad Hossein Samavatian ; Anys Bacha ; Li Zhou ; Radu Teodorescu
COMMENTS:       26 pages
HIGHLIGHT:      This work presents RNNFast, a hardware accelerator for RNNs that leverages an emerging class of non-volatile memory called domain-wall memory (DWM).

37, TITLE:      Mean-Variance Policy Iteration for Risk-Averse Reinforcement Learning
http://arxiv.org/abs/2004.10888
AUTHORS:        Shangtong Zhang ; Bo Liu ; Shimon Whiteson
HIGHLIGHT:      We present a mean-variance policy iteration (MVPI) framework for risk-averse control in a discounted infinite horizon MDP.

38, TITLE:      "You might also like this model": Data Driven Approach for Recommending Deep Learning Models for Unknown Image Datasets
http://arxiv.org/abs/1911.11433
AUTHORS:        Ameya Prabhu ; Riddhiman Dasgupta ; Anush Sankaran ; Srikanth Tamilselvam ; Senthil Mani
COMMENTS:       NeurIPS 2019, New in ML Group
HIGHLIGHT:      In this research, we propose a novel technique to recommend a suitable architecture from a repository of known models.

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

40, TITLE:      Unsupervised Morphological Paradigm Completion
http://arxiv.org/abs/2005.00970
AUTHORS:        Huiming Jin ; Liwei Cai ; Yihui Peng ; Chen Xia ; Arya D. McCarthy ; Katharina Kann
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      We propose the task of unsupervised morphological paradigm completion.

41, TITLE:      Unsupervised Domain Adaptation of Language Models for Reading Comprehension
http://arxiv.org/abs/1911.10768
AUTHORS:        Kosuke Nishida ; Kyosuke Nishida ; Itsumi Saito ; Hisako Asano ; Junji Tomita
COMMENTS:       LREC2020
HIGHLIGHT:      To solve the UDARC problem, we provide two domain adaptation models.

42, TITLE:      Image Embedded Segmentation: Uniting Supervised and Unsupervised Objectives for Segmenting Histopathological Images
http://arxiv.org/abs/2001.11202
AUTHORS:        C. T. Sari ; C. Sokmensuer ; C. Gunduz-Demir
COMMENTS:       This work has been submitted to the IEEE for possible publication
HIGHLIGHT:      This paper presents a new regularization method to train a fully convolutional network for semantic tissue segmentation in histopathological images.

43, TITLE:      4D Generic Video Object Proposals
http://arxiv.org/abs/1901.09260
AUTHORS:        Aljosa Osep ; Paul Voigtlaender ; Mark Weber ; Jonathon Luiten ; Bastian Leibe
COMMENTS:       ICRA 2020
HIGHLIGHT:      We propose an approach that can reliably extract spatio-temporal object proposals for both known and unknown object categories from stereo video.

44, TITLE:      Detailed 2D-3D Joint Representation for Human-Object Interaction
http://arxiv.org/abs/2004.08154
AUTHORS:        Yong-Lu Li ; Xinpeng Liu ; Han Lu ; Shiyi Wang ; Junqi Liu ; Jiefeng Li ; Cewu Lu
COMMENTS:       Accepted to CVPR 2020, supplementary materials included, code available:https://github.com/DirtyHarryLYL/DJ-RN
HIGHLIGHT:      In light of these, we propose a detailed 2D-3D joint representation learning method. To better evaluate the 2D ambiguity processing capacity of models, we propose a new benchmark named Ambiguous-HOI consisting of hard ambiguous images.

45, TITLE:      Finding Covid-19 from Chest X-rays using Deep Learning on a Small Dataset
http://arxiv.org/abs/2004.02060
AUTHORS:        Lawrence O. Hall ; Rahul Paul ; Dmitry B. Goldgof ; Gregory M. Goldgof
COMMENTS:       6 pages
HIGHLIGHT:      This paper explores how useful chest X-ray images can be in diagnosing COVID-19 disease.

46, TITLE:      TF-Coder: Program Synthesis for Tensor Manipulations
http://arxiv.org/abs/2003.09040
AUTHORS:        Kensen Shi ; David Bieber ; Rishabh Singh
HIGHLIGHT:      In this work, we present a tool called TF-Coder for programming by example in TensorFlow.

47, TITLE:      A Deep Unsupervised Feature Learning Spiking Neural Network with Binarized Classification Layers for EMNIST Classification using SpykeFlow
http://arxiv.org/abs/2002.11843
AUTHORS:        Ruthvik Vaila ; John Chiasson ; Vishal Saxena
COMMENTS:       A section of of this work is Submitted to IEEE TETCI 2020 Journal
HIGHLIGHT:      In this work we approach this using spiking neural networks.

48, TITLE:      A Top-Down Neural Architecture towards Text-Level Parsing of Discourse Rhetorical Structure
http://arxiv.org/abs/2005.02680
AUTHORS:        Longyin Zhang ; Yuqing Xing ; Fang Kong ; Peifeng Li ; Guodong Zhou
COMMENTS:       Accepted by ACL2020
HIGHLIGHT:      In this paper, we justify from both computational and perceptive points-of-view that the top-down architecture is more suitable for text-level DRS parsing.

49, TITLE:      Classifying Suspicious Content in Tor Darknet
http://arxiv.org/abs/2005.10086
AUTHORS:        Eduardo Fidalgo Fernandez ; Roberto Andrés Vasco Carofilis ; Francisco Jáñez Martino ; Pablo Blanco Medina
COMMENTS:       To be published on the JNIC 2020 Conference. Summary of already published research
HIGHLIGHT:      To solve this problem, in this paper, we explore the automatic classification Tor Darknet images using Semantic Attention Keypoint Filtering, a strategy that filters non-significant features at a pixel level that do not belong to the object of interest, by combining saliency maps with Bag of Visual Words (BoVW).

50, TITLE:      Learning Structural Graph Layouts and 3D Shapes for Long Span Bridges 3D Reconstruction
http://arxiv.org/abs/1907.03387
AUTHORS:        Fangqiao Hu ; Jin Zhao ; Yong Huang ; Hui Li
HIGHLIGHT:      A learning-based 3D reconstruction method for long-span bridges is proposed in this paper.

51, TITLE:      Gradient-Based Adversarial Training on Transformer Networks for Detecting Check-Worthy Factual Claims
http://arxiv.org/abs/2002.07725
AUTHORS:        Kevin Meng ; Damian Jimenez ; Fatma Arslan ; Jacob Daniel Devasier ; Daniel Obembe ; Chengkai Li
COMMENTS:       11 pages, 4 figures, 6 tables
HIGHLIGHT:      In the process, we propose a method to apply adversarial training to transformer models, which has the potential to be generalized to many similar text classification tasks. Along with our results, we are releasing our codebase and manually labeled datasets.

52, TITLE:      Multipurpose Intelligent Process Automation via Conversational Assistant
http://arxiv.org/abs/2001.02284
AUTHORS:        Alena Moiseeva ; Dietrich Trautmann ; Michael Heimann ; Hinrich Schütze
COMMENTS:       Presented at the AAAI-20 Workshop on Intelligent Process Automation
HIGHLIGHT:      In this work, we tackle a challenge of implementing an IPA conversational assistant in a real-world industrial setting with a lack of structured training data.

53, TITLE:      Classification of Industrial Control Systems screenshots using Transfer Learning
http://arxiv.org/abs/2005.10098
AUTHORS:        Pablo Blanco Medina ; Eduardo Fidalgo Fernandez ; Enrique Alegre ; Francisco Jáñez Martino ; Roberto A. Vasco-Carofilis ; Víctor Fidalgo Villar
COMMENTS:       To be published on the JNIC 2020 Conference
HIGHLIGHT:      In order to solve this problem, we use transfer learning with five CNN architectures, pre-trained on Imagenet, to determine which one best classifies screenshots obtained from Industrial Controls Systems.

54, TITLE:      Perceptual Hashing applied to Tor domains recognition
http://arxiv.org/abs/2005.10090
AUTHORS:        Rubel Biswas ; Roberto A. Vasco-Carofilis ; Eduardo Fidalgo Fernandez ; Francisco Jáñez Martino ; Pablo Blanco Medina
COMMENTS:       To be published on the JNIC 2020 Conference. Already published research summary
HIGHLIGHT:      To support this task, we introduce Frequency-Dominant Neighborhood Structure (F-DNS), a new perceptual hashing method for automatically classifying domains by their screenshots.

55, TITLE:      BLEURT: Learning Robust Metrics for Text Generation
http://arxiv.org/abs/2004.04696
AUTHORS:        Thibault Sellam ; Dipanjan Das ; Ankur P. Parikh
COMMENTS:       Accepted at ACL 2020
HIGHLIGHT:      We propose BLEURT, a learned evaluation metric based on BERT that can model human judgments with a few thousand possibly biased training examples.

56, TITLE:      Improving Semantic Segmentation through Spatio-Temporal Consistency Learned from Videos
http://arxiv.org/abs/2004.05324
AUTHORS:        Ankita Pasad ; Ariel Gordon ; Tsung-Yi Lin ; Anelia Angelova
COMMENTS:       Learning from Unlabeled Videos, CVPR Workshop, 2020
HIGHLIGHT:      We leverage unsupervised learning of depth, egomotion, and camera intrinsics to improve the performance of single-image semantic segmentation, by enforcing 3D-geometric and temporal consistency of segmentation masks across video frames.