2020.05.18.txt

==========New Papers==========
1, TITLE:       Neural Entity Linking on Technical Service Tickets
http://arxiv.org/abs/2005.07604
AUTHORS:        Nadja Kurz ; Felix Hamann ; Adrian Ulges
HIGHLIGHT:      Using an entity linking model based on BERT, a popular transformer network in natural language processing, we show that a neural approach outperforms and complements hand-coded heuristics, with improvements of about 20\% top-1 accuracy.

2, TITLE:       Challenges in Emotion Style Transfer: An Exploration with a Lexical Substitution Pipeline
http://arxiv.org/abs/2005.07617
AUTHORS:        David Helbig ; Enrica Troiano ; Roman Klinger
COMMENTS:       Accepted at the SocialNLP Workshop at ACL 2020
HIGHLIGHT:      We propose the task of emotion style transfer, which is particularly challenging, as emotions (here: anger, disgust, fear, joy, sadness, surprise) are on the fence between content and style.

3, TITLE:       Think Too Fast Nor Too Slow: The Computational Trade-off Between Planning And Reinforcement Learning
http://arxiv.org/abs/2005.07404
AUTHORS:        Thomas M. Moerland ; Anna Deichler ; Simone Baldi ; Joost Broekens ; Catholijn M. Jonker
HIGHLIGHT:      Conceptually, we identify a new spectrum of planning-learning algorithms which ranges from exhaustive search (long planning) to model-free RL (no planning), with optimal performance achieved midway.

4, TITLE:       Benchmarking neural embeddings for link prediction in knowledge graphs under semantic and structural changes
http://arxiv.org/abs/2005.07654
AUTHORS:        Asan Agibetov ; Matthias Samwald
HIGHLIGHT:      In this work we propose an open-source evaluation pipeline, which benchmarks the accuracy of neural embeddings in situations where knowledge graphs may experience semantic and structural changes.

5, TITLE:       Analyzing Temporal Relationships between Trending Terms on Twitter and Urban Dictionary Activity
http://arxiv.org/abs/2005.07655
AUTHORS:        Steven R. Wilson ; Walid Magdy ; Barbara McGillivray ; Gareth Tyson
COMMENTS:       Accepted at The Web Science Conference 2020
HIGHLIGHT:      In this research, we study the temporal activity trends on Urban Dictionary and provide the first analysis of how this activity relates to content being discussed on a major social network: Twitter.

6, TITLE:       MineReduce: an approach based on data mining for problem size reduction
http://arxiv.org/abs/2005.07415
AUTHORS:        Marcelo Rodrigues de Holanda Maia ; Alexandre Plastino ; Puca Huachi Vaz Penna
HIGHLIGHT:      In this paper, we build upon these ideas by presenting an approach named MineReduce, which uses mined patterns to perform problem size reduction.

7, TITLE:       Finding Experts in Transformer Models
http://arxiv.org/abs/2005.07647
AUTHORS:        Xavier Suau ; Luca Zappella ; Nicholas Apostoloff
HIGHLIGHT:      In this work we study the presence of expert units in pre-trained Transformer Models (TM), and how they impact a model's performance.

8, TITLE:       Grounding Language in Play
http://arxiv.org/abs/2005.07648
AUTHORS:        Corey Lynch ; Pierre Sermanet
HIGHLIGHT:      In this work we present a simple and scalable way to condition policies on human language instead.

9, TITLE:       ResMoNet: A Residual Mobile-based Network for Facial Emotion Recognition in Resource-Limited Systems
http://arxiv.org/abs/2005.07649
AUTHORS:        Rodolfo Ferro-Pérez ; Hugo Mitre-Hernandez
COMMENTS:       11 pages, 7 Figures
HIGHLIGHT:      In this paper, we compare our proposed model inspired in depthwise separable convolutions and residual blocks with MobileNet, PeleeNet, EDNN and IDNN.

10, TITLE:      Guided interactive image segmentation using machine learning and color based data set clustering
http://arxiv.org/abs/2005.07662
AUTHORS:        Adrian Friebel ; Tim Johann ; Dirk Drasdo ; Stefan Hoehme
HIGHLIGHT:      We present a novel approach that combines machine learning based interactive image segmentation with a two-stage clustering method for identification of similarly colored images enabling efficient batch image segmentation through guided reuse of interactively trained classifiers.

11, TITLE:      Spelling Error Correction with Soft-Masked BERT
http://arxiv.org/abs/2005.07421
AUTHORS:        Shaohua Zhang ; Haoran Huang ; Jicong Liu ; Hang Li
COMMENTS:       To be published at ACL 2020
HIGHLIGHT:      In this work, we propose a novel neural architecture to address the aforementioned issue, which consists of a network for error detection and a network for error correction based on BERT, with the former being connected to the latter with what we call soft-masking technique.

12, TITLE:      DeepSoCS: A Neural Scheduler for Heterogeneous System-on-Chip Resource Scheduling
http://arxiv.org/abs/2005.07666
AUTHORS:        Tegg Taekyong Sung ; Jeongsoo Ha ; Jeewoo Kim ; Alex Yahja ; Bo Ryu
COMMENTS:       18 pages
HIGHLIGHT:      In this paper, we present a novel scheduling solution for a class of System-on-Chip (SoC) systems where heterogeneous chip resources (DSP, FPGA, GPU, etc.) must be efficiently scheduled for continuously arriving hierarchical jobs with their tasks represented by directed acyclic graph.

13, TITLE:      Exploring the Capabilities and Limits of 3D Monocular Object Detection -- A Study on Simulation and Real World Data
http://arxiv.org/abs/2005.07424
AUTHORS:        Felix Nobis ; Fabian Brunhuber ; Simon Janssen ; Johannes Betz ; Markus Lienkamp
COMMENTS:       Accepted at The 23rd IEEE International Conference on Intelligent Transportation Systems, September 20 - 23, 2020
HIGHLIGHT:      In this paper, we evaluate the performance of a 3D object detection pipeline which is parameterizable with different depth estimation configurations.

14, TITLE:      Recent Advances in SQL Query Generation: A Survey
http://arxiv.org/abs/2005.07667
AUTHORS:        Jovan Kalajdjieski ; Martina Toshevska ; Frosina Stojanovska
COMMENTS:       Part of the 17th International Conference on Informatics and Information Technologies. Received best paper award
HIGHLIGHT:      We describe models with various architectures such as convolutional neural networks, recurrent neural networks, pointer networks, reinforcement learning, etc.

15, TITLE:      A Deep Learning-based Radar and Camera Sensor Fusion Architecture for Object Detection
http://arxiv.org/abs/2005.07431
AUTHORS:        Felix Nobis ; Maximilian Geisslinger ; Markus Weber ; Johannes Betz ; Markus Lienkamp
COMMENTS:       Accepted at 2019 Sensor Data Fusion: Trends, Solutions, Applications (SDF)
HIGHLIGHT:      Rising detection rates and computationally efficient network structures are pushing this technique towards application in production vehicles.

16, TITLE:      Finding Game Levels with the Right Difficulty in a Few Trials through Intelligent Trial-and-Error
http://arxiv.org/abs/2005.07677
AUTHORS:        Miguel González-Duque ; Rasmus Berg Palm ; David Ha ; Sebastian Risi
HIGHLIGHT:      This paper presents a method that can generate and search for complete levels with a specific target difficulty in only a few trials.

17, TITLE:      Optimizing Neural Architecture Search using Limited GPU Time in a Dynamic Search Space: A Gene Expression Programming Approach
http://arxiv.org/abs/2005.07669
AUTHORS:        Jeovane Honorio Alves ; Lucas Ferrari de Oliveira
COMMENTS:       Accepted for presentation at the IEEE Congress on Evolutionary Computation (IEEE CEC) 2020
HIGHLIGHT:      In this paper, we propose an evolutionary-based neural architecture search approach for efficient discovery of convolutional models in a dynamic search space, within only 24 GPU hours.

18, TITLE:      Persistent Map Saving for Visual Localization for Autonomous Vehicles: An ORB-SLAM Extension
http://arxiv.org/abs/2005.07429
AUTHORS:        Felix Nobis ; Odysseas Papanikolaou ; Johannes Betz ; Markus Lienkamp
COMMENTS:       Accepted at 2020 Fifteenth International Conference on Ecological Vehicles and Renewable Energies (EVER)
HIGHLIGHT:      In this paper, we make use of a stereo camera sensor in order to perceive the environment and create the map.

19, TITLE:      Small-brain neural networks rapidly solve inverse problems with vortex Fourier encoders
http://arxiv.org/abs/2005.07682
AUTHORS:        Baurzhan Muminov ; Luat T. Vuong
HIGHLIGHT:      We introduce a vortex phase transform with a lenslet-array to accompany shallow, dense, ``small-brain'' neural networks for high-speed and low-light imaging.

20, TITLE:      Movement Pruning: Adaptive Sparsity by Fine-Tuning
http://arxiv.org/abs/2005.07683
AUTHORS:        Victor Sanh ; Thomas Wolf ; Alexander M. Rush
COMMENTS:       12 pages, 6 figures, 3 tables
HIGHLIGHT:      We propose the use of movement pruning, a simple, deterministic first-order weight pruning method that is more adaptive to pretrained model fine-tuning.

21, TITLE:      A pre-training technique to localize medical BERT and enhance BioBERT
http://arxiv.org/abs/2005.07202
AUTHORS:        Shoya Wada ; Toshihiro Takeda ; Shiro Manabe ; Shozo Konishi ; Jun Kamohara ; Yasushi Matsumura
COMMENTS:       We made the pre-trained weights of ouBioBERT and the source code for fine-tuning freely available at https://github.com/sy-wada/blue_benchmark_with_transformers
HIGHLIGHT:      Therefore, we propose a method that realizes a high-performance BERT model by using a small corpus.

22, TITLE:      Cross-lingual Transfer of Twitter Sentiment Models Using a Common Vector Space
http://arxiv.org/abs/2005.07456
AUTHORS:        Marko Robnik-Sikonja ; Igor Mozetic
HIGHLIGHT:      We use cross-lingual word embeddings to transfer machine learning prediction models for Twitter sentiment between 13 languages.

23, TITLE:      PrimiTect: Fast Continuous Hough Voting for Primitive Detection
http://arxiv.org/abs/2005.07457
AUTHORS:        Christiane Sommer ; Yumin Sun ; Erik Bylow ; Daniel Cremers
COMMENTS:       Accepted to IEEE International Conference on Robotics and Automation (ICRA), 2020 | Code: https://github.com/c-sommer/primitect
HIGHLIGHT:      This paper tackles the problem of data abstraction in the context of 3D point sets.

24, 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.

25, TITLE:      An Object Model for the Representation of Empirical Knowledge
http://arxiv.org/abs/2005.07464
AUTHORS:        Joël Colloc ; Danielle Boulanger
COMMENTS:       in French. Colloque International ICO'89, Jun 1989, Quebec, Canada
HIGHLIGHT:      We are currently designing an object oriented model which describes static and dynamical knowledge in diff{\'e}rent domains.

26, TITLE:      SAGE: Sequential Attribute Generator for Analyzing Glioblastomas using Limited Dataset
http://arxiv.org/abs/2005.07225
AUTHORS:        Padmaja Jonnalagedda ; Brent Weinberg ; Jason Allen ; Taejin L. Min ; Shiv Bhanu ; Bir Bhanu
HIGHLIGHT:      In this paper, we detect imaging biomarkers for the mutation to streamline the extensive and invasive prognosis pipeline.

27, TITLE:      Predicting User Emotional Tone in Mental Disorder Online Communities
http://arxiv.org/abs/2005.07473
AUTHORS:        Bárbara Silveira ; Fabricio Murai ; Ana Paula Couto da Silva
COMMENTS:       8 pages, 3 figures, 3 tables
HIGHLIGHT:      Here we analyze how Reddit discussions can help improve the health conditions of its users.

28, TITLE:      Direction-aware Residual Network for Road Extraction in VHR Remote Sensing Images
http://arxiv.org/abs/2005.07232
AUTHORS:        Lei Ding ; Lorenzo Bruzzone
COMMENTS:       10 pages, 9 figures. To be submitted to IEEE TGRS
HIGHLIGHT:      In this paper, we consider the specific context of road extraction and present a direction-aware residual network that includes three main contributions: 1) ResDec: an asymmetric residual segmentation network with deconvolutional layers and a structural supervision to enhance the learning of road topology; 2) a pixel-level supervision of local directions to enhance the embedding of linear features; 3) Refnet: a refinement network to optimize the segmentation results.

29, TITLE:      Convex Shape Prior for Deep Neural Convolution Network based Eye Fundus Images Segmentation
http://arxiv.org/abs/2005.07476
AUTHORS:        Jun Liu ; Xue-Cheng Tai ; Shousheng Luo
HIGHLIGHT:      In this work, we propose a technique which can be easily integrated into the commonly used DCNNs for image segmentation and guarantee that outputs are convex shapes.

30, TITLE:      Evo* 2020 -- Late-Breaking Abstracts Volume
http://arxiv.org/abs/2005.07235
AUTHORS:        A. M. Mora ; A. I. Esparcia-Alcázar
COMMENTS:       LBAs accepted in Evo* 2020. Part of the Conference Proceedings
HIGHLIGHT:      All of them present ongoing research and preliminary results investigating on the application of different approaches of Bioinspired Methods (mainly Evolutionary Computation) to different problems, most of them real world ones.

31, TITLE:      Mixed-Initiative Procedural Content Generation using Level Design Patterns and Interactive Evolutionary Optimisation
http://arxiv.org/abs/2005.07478
AUTHORS:        Sean P. Walton ; Alma A. M. Rahat ; James Stovold
HIGHLIGHT:      An approach for building mixed-initiative tools for the procedural generation of game levels using interactive evolutionary optimisation is introduced.

32, TITLE:      Evolved Explainable Classifications for Lymph Node Metastases
http://arxiv.org/abs/2005.07229
AUTHORS:        Iam Palatnik de Sousa ; Marley Maria Bernardes Rebuzzi Vellasco ; Eduardo Costa da Silva
HIGHLIGHT:      A novel evolutionary approach for Explainable Artificial Intelligence is presented: the "Evolved Explanations" model (EvEx).

33, TITLE:      Adaptive Transformers for Learning Multimodal Representations
http://arxiv.org/abs/2005.07486
AUTHORS:        Prajjwal Bhargava
COMMENTS:       Accepted at ACL SRW 2020. Code can be found here https://github.com/prajjwal1/adaptive_transformer
HIGHLIGHT:      In this work, we extend adaptive approaches to learn more about model interpretability and computational efficiency.

34, TITLE:      Target-Speaker Voice Activity Detection: a Novel Approach for Multi-Speaker Diarization in a Dinner Party Scenario
http://arxiv.org/abs/2005.07272
AUTHORS:        Ivan Medennikov ; Maxim Korenevsky ; Tatiana Prisyach ; Yuri Khokhlov ; Mariya Korenevskaya ; Ivan Sorokin ; Tatiana Timofeeva ; Anton Mitrofanov ; Andrei Andrusenko ; Ivan Podluzhny ; Aleksandr Laptev ; Aleksei Romanenko
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We propose a novel Target-Speaker Voice Activity Detection (TS-VAD) approach, which directly predicts an activity of each speaker on each time frame.

35, TITLE:      Bi3D: Stereo Depth Estimation via Binary Classifications
http://arxiv.org/abs/2005.07274
AUTHORS:        Abhishek Badki ; Alejandro Troccoli ; Kihwan Kim ; Jan Kautz ; Pradeep Sen ; Orazio Gallo
COMMENTS:       To be presented at CVPR 2020
HIGHLIGHT:      We present Bi3D, a method that estimates depth via a series of binary classifications.

36, TITLE:      SUPER: A Novel Lane Detection System
http://arxiv.org/abs/2005.07277
AUTHORS:        Pingping Lu ; Chen Cui ; Shaobing Xu ; Huei Peng ; Fan Wang
HIGHLIGHT:      In this paper, we propose a real-time lane detection system, called Scene Understanding Physics-Enhanced Real-time (SUPER) algorithm.

37, TITLE:      VirAAL: Virtual Adversarial Active Learning
http://arxiv.org/abs/2005.07287
AUTHORS:        Gregory Senay ; Badr Youbi Idrissi ; Marine Haziza
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      This paper presents VirAAL, an Active Learning framework based on Adversarial Training.

38, TITLE:      Taskology: Utilizing Task Relations at Scale
http://arxiv.org/abs/2005.07289
AUTHORS:        Yao Lu ; Sören Pirk ; Jan Dlabal ; Anthony Brohan ; Ankita Pasad ; Zhao Chen ; Vincent Casser ; Anelia Angelova ; Ariel Gordon
HIGHLIGHT:      To this end, we aim to establish and explore a novel approach for the collective training of computer vision tasks.

39, TITLE:      Enhancing Perceptual Loss with Adversarial Feature Matching for Super-Resolution
http://arxiv.org/abs/2005.07502
AUTHORS:        Akella Ravi Tej ; Shirsendu Sukanta Halder ; Arunav Pratap Shandeelya ; Vinod Pankajakshan
COMMENTS:       Accepted for publication in the International Joint Conference on Neural Networks (IJCNN) 2020
HIGHLIGHT:      In this paper, we show that the root cause of these pattern artifacts can be traced back to a mismatch between the pre-training objective of perceptual loss and the super-resolution objective.

40, TITLE:      COVID-Twitter-BERT: A Natural Language Processing Model to Analyse COVID-19 Content on Twitter
http://arxiv.org/abs/2005.07503
AUTHORS:        Martin Müller ; Marcel Salathé ; Per E Kummervold
HIGHLIGHT:      In this work, we release COVID-Twitter-BERT (CT-BERT), a transformer-based model, pretrained on a large corpus of Twitter messages on the topic of COVID-19.

41, TITLE:      A Distributional View on Multi-Objective Policy Optimization
http://arxiv.org/abs/2005.07513
AUTHORS:        Abbas Abdolmaleki ; Sandy H. Huang ; Leonard Hasenclever ; Michael Neunert ; H. Francis Song ; Martina Zambelli ; Murilo F. Martins ; Nicolas Heess ; Raia Hadsell ; Martin Riedmiller
HIGHLIGHT:      In this paper we propose a novel algorithm for multi-objective reinforcement learning that enables setting desired preferences for objectives in a scale-invariant way.

42, TITLE:      Corpus and Models for Lemmatisation and POS-tagging of Classical French Theatre
http://arxiv.org/abs/2005.07505
AUTHORS:        Jean-Baptiste Camps ; Simon Gabay ; Paul Fièvre ; Thibault Clérice ; Florian Cafiero
HIGHLIGHT:      This paper describes the process of building an annotated corpus and training models for classical French literature, with a focus on theatre, and particularly comedies in verse.

43, TITLE:      Simple Sensor Intentions for Exploration
http://arxiv.org/abs/2005.07541
AUTHORS:        Tim Hertweck ; Martin Riedmiller ; Michael Bloesch ; Jost Tobias Springenberg ; Noah Siegel ; Markus Wulfmeier ; Roland Hafner ; Nicolas Heess
HIGHLIGHT:      In this paper we focus on a setting in which goal tasks are defined via simple sparse rewards, and exploration is facilitated via agent-internal auxiliary tasks.

44, TITLE:      Investigating Bias in Deep Face Analysis: The KANFace Dataset and Empirical Study
http://arxiv.org/abs/2005.07302
AUTHORS:        Markos Georgopoulos ; Yannis Panagakis ; Maja Pantic
HIGHLIGHT:      In this work, we investigate the demographic bias of deep learning models in face recognition, age estimation, gender recognition and kinship verification. To this end, we introduce the most comprehensive, large-scale dataset of facial images and videos to date.

45, TITLE:      3D deformable registration of longitudinal abdominopelvic CT images using unsupervised deep learning
http://arxiv.org/abs/2005.07545
AUTHORS:        Maureen van Eijnatten ; Leonardo Rundo ; K. Joost Batenburg ; Felix Lucka ; Emma Beddowes ; Carlos Caldas ; Ferdia A. Gallagher ; Evis Sala ; Carola-Bibiane Schönlieb ; Ramona Woitek
HIGHLIGHT:      This study investigates the use of the unsupervised deep learning framework VoxelMorph for deformable registration of longitudinal abdominopelvic CT images acquired in patients with bone metastases from breast cancer.

46, TITLE:      Behind the Scene: Revealing the Secrets of Pre-trained Vision-and-Language Models
http://arxiv.org/abs/2005.07310
AUTHORS:        Jize Cao ; Zhe Gan ; Yu Cheng ; Licheng Yu ; Yen-Chun Chen ; Jingjing Liu
HIGHLIGHT:      To reveal the secrets behind the scene of these powerful models, we present VALUE (Vision-And-Language Understanding Evaluation), a set of meticulously designed probing tasks (e.g., Visual Coreference Resolution, Visual Relation Detection, Linguistic Probing Tasks) generalizable to standard pre-trained V+L models, aiming to decipher the inner workings of multimodal pre-training (e.g., the implicit knowledge garnered in individual attention heads, the inherent cross-modal alignment learned through contextualized multimodal embeddings).

47, TITLE:      HNAS: Hierarchical Neural Architecture Search on Mobile Devices
http://arxiv.org/abs/2005.07564
AUTHORS:        Xin Xia ; Wenrui Ding
COMMENTS:       16 pages, 7 figures
HIGHLIGHT:      To address this issue, in this paper, we propose a new method, named Hierarchical Neural Architecture Search (HNAS).

48, TITLE:      ViTAA: Visual-Textual Attributes Alignment in Person Search by Natural Language
http://arxiv.org/abs/2005.07327
AUTHORS:        Zhe Wang ; Zhiyuan Fang ; Jun Wang ; Yezhou Yang
COMMENTS:       18 pages, 6 figures
HIGHLIGHT:      We achieve success as well as the performance boosting by a robust feature learning that the referred identity can be accurately bundled by multiple attribute visual cues.

49, TITLE:      Exploring Crowd Co-creation Scenarios for Sketches
http://arxiv.org/abs/2005.07328
AUTHORS:        Devi Parikh ; C. Lawrence Zitnick
HIGHLIGHT:      As a first step towards studying the ability of human crowds and machines to effectively co-create, we explore several human-only collaborative co-creation scenarios.

50, TITLE:      Visual Perception Model for Rapid and Adaptive Low-light Image Enhancement
http://arxiv.org/abs/2005.07343
AUTHORS:        Xiaoxiao Li ; Xiaopeng Guo ; Liye Mei ; Mingyu Shang ; Jie Gao ; Maojing Shu ; Xiang Wang
COMMENTS:       Due to the limitation "The abstract field cannot be longer than 1,920 characters", the abstract here is shorter than that in the PDF file
HIGHLIGHT:      By coordinating the proposed VP model, illumination and reflectance estimation scheme, and the optimal determination strategy, we propose a rapid and adaptive framework for low-light image enhancement.

51, TITLE:      Resisting the Distracting-factors in Pedestrian Detection
http://arxiv.org/abs/2005.07344
AUTHORS:        Zhe Wang ; Jun Wang ; Yezhou Yang
COMMENTS:       17 pages, 5 figures
HIGHLIGHT:      For appearance distraction, we propose an efficient semantic-driven strategy for selecting anchor locations, which can sample informative negative examples at training phase for classification refinement.

52, TITLE:      A chatbot architecture for promoting youth resilience
http://arxiv.org/abs/2005.07355
AUTHORS:        Chester Holt-Quick ; Jim Warren ; Karolina Stasiak ; Ruth Williams ; Grant Christie ; Sarah Hetrick ; Sarah Hopkins ; Tania Cargo ; Sally Merry
COMMENTS:       7 pages, 4 figures, submitted to Australian Health Informatics Conference, Brisbane, October 2020
HIGHLIGHT:      This paper describes the architecture underlying the chatbot.

53, TITLE:      Near-duplicate video detection featuring coupled temporal and perceptual visual structures and logical inference based matching
http://arxiv.org/abs/2005.07356
AUTHORS:        B. Tahayna ; M. Belkhatir
HIGHLIGHT:      We propose in this paper an architecture for near-duplicate video detection based on: (i) index and query signature based structures integrating temporal and perceptual visual features and (ii) a matching framework computing the logical inference between index and query documents.

54, TITLE:      Learning Rate Annealing Can Provably Help Generalization, Even for Convex Problems
http://arxiv.org/abs/2005.07360
AUTHORS:        Preetum Nakkiran
COMMENTS:       4 pages plus appendix
HIGHLIGHT:      In this note, we show that this phenomenon can exist even for convex learning problems -- in particular, linear regression in 2 dimensions.

55, TITLE:      Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
http://arxiv.org/abs/2005.07362
AUTHORS:        Ryuichi Takanobu ; Qi Zhu ; Jinchao Li ; Baolin Peng ; Jianfeng Gao ; Minlie Huang
COMMENTS:       SIGDIAL 2020 long paper
HIGHLIGHT:      In this paper, we perform a system-wise evaluation and present an empirical analysis on different types of dialog systems which are composed of different modules in different settings.

56, TITLE:      Lifelong Multi-Agent Path Finding in Large-Scale Warehouses
http://arxiv.org/abs/2005.07371
AUTHORS:        Jiaoyang Li ; Andrew Tinka ; Scott Kiesel ; Joseph W. Durham ; T. K. Satish Kumar ; Sven Koenig
HIGHLIGHT:      In this paper, we study the lifelong variant of MAPF where agents are constantly engaged with new goal locations, such as in large-scale warehouses.

57, TITLE:      Improving Neuroevolution Using Island Extinction and Repopulation
http://arxiv.org/abs/2005.07376
AUTHORS:        Zimeng Lyu ; Joshua Karns ; AbdElRahman ElSaid ; Travis Desell
HIGHLIGHT:      In this paper, we propose utilizing extinction events and island repopulation to avoid premature convergence.

58, TITLE:      Semi-supervised Medical Image Classification with Relation-driven Self-ensembling Model
http://arxiv.org/abs/2005.07377
AUTHORS:        Quande Liu ; Lequan Yu ; Luyang Luo ; Qi Dou ; Pheng Ann Heng
COMMENTS:       IEEE Transactions on Medical Imaging, 2020
HIGHLIGHT:      In this paper, we present a novel relation-driven semi-supervised framework for medical image classification.

59, TITLE:      Enhancing Lattice-based Motion Planning with Introspective Learning and Reasoning
http://arxiv.org/abs/2005.07385
AUTHORS:        Mattias Tiger ; David Bergström ; Andreas Norrstig ; Fredrik Heintz
HIGHLIGHT:      In this work we are concerned with introspective learning and reasoning about controller performance over time.

60, TITLE:      Contextualizing ASR Lattice Rescoring with Hybrid Pointer Network Language Model
http://arxiv.org/abs/2005.07394
AUTHORS:        Da-Rong Liu ; Chunxi Liu ; Frank Zhang ; Gabriel Synnaeve ; Yatharth Saraf ; Geoffrey Zweig
HIGHLIGHT:      In this paper, we explore ASR lattice rescoring by selectively attending to the video descriptions.

61, TITLE:      Statistical Equity: A Fairness Classification Objective
http://arxiv.org/abs/2005.07293
AUTHORS:        Ninareh Mehrabi ; Yuzhong Huang ; Fred Morstatter
HIGHLIGHT:      We propose a new fairness definition, motivated by the principle of equity, that considers existing biases in the data and attempts to make equitable decisions that account for these previous historical biases.

62, TITLE:      OSACT4 Shared Task on Offensive Language Detection: Intensive Preprocessing-Based Approach
http://arxiv.org/abs/2005.07297
AUTHORS:        Fatemah Husain
COMMENTS:       Proceedings of the Twelfth International Conference on Language Resources and Evaluation (LREC 2020), Marseille, France (2020)
HIGHLIGHT:      This study aims at investigating the impact of the preprocessing phase on text classification, specifically on offensive language and hate speech classification for Arabic text.

63, TITLE:      DeepFaceFlow: In-the-wild Dense 3D Facial Motion Estimation
http://arxiv.org/abs/2005.07298
AUTHORS:        Mohammad Rami Koujan ; Anastasios Roussos ; Stefanos Zafeiriou
COMMENTS:       to be published in the IEEE conference on Computer Vision and Pattern Recognition (CVPR). 2020
HIGHLIGHT:      In this work, we propose DeepFaceFlow, a robust, fast, and highly-accurate framework for the dense estimation of 3D non-rigid facial flow between pairs of monocular images.



==========Updates to Previous Papers==========
1, TITLE:       A Survey on Temporal Reasoning for Temporal Information Extraction from Text (Extended Abstract)
http://arxiv.org/abs/2005.06527
AUTHORS:        Artuur Leeuwenberg ; Marie-Francine Moens
COMMENTS:       Extended abstract of a JAIR article, which is to appear in the proceedings of IJCAI 2020 (the copyright of this abstract is held by IJCAI 2020)
HIGHLIGHT:      This article presents a comprehensive survey of the research from the past decades on temporal reasoning for automatic temporal information extraction from text, providing a case study on the integration of symbolic reasoning with machine learning-based information extraction systems.

2, TITLE:       VOC-ReID: Vehicle Re-identification based on Vehicle-Orientation-Camera
http://arxiv.org/abs/2004.09164
AUTHORS:        Xiangyu Zhu ; Zhenbo Luo ; Pei Fu ; Xiang Ji
COMMENTS:       AICity2020 Challenge, CVPR 2020 workshop, code avaible at github(link in abstract)
HIGHLIGHT:      In this work, we focus on the failure cases caused by similar background and shape.

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

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

5, TITLE:       Arabic Dialect Identification in the Wild
http://arxiv.org/abs/2005.06557
AUTHORS:        Ahmed Abdelali ; Hamdy Mubarak ; Younes Samih ; Sabit Hassan ; Kareem Darwish
COMMENTS:       13 pages, 7 figures, 4 tables
HIGHLIGHT:      We present QADI, an automatically collected dataset of tweets belonging to a wide range of country-level Arabic dialects -covering 18 different countries in the Middle East and North Africa region.

6, TITLE:       Binarizing MobileNet via Evolution-based Searching
http://arxiv.org/abs/2005.06305
AUTHORS:        Hai Phan ; Zechun Liu ; Dang Huynh ; Marios Savvides ; Kwang-Ting Cheng ; Zhiqiang Shen
COMMENTS:       Accepted by CVPR2020
HIGHLIGHT:      In this paper, we propose a use of evolutionary search to facilitate the construction and training scheme when binarizing MobileNet, a compact network with separable depth-wise convolution.

7, TITLE:       Representing fitness landscapes by valued constraints to understand the complexity of local search
http://arxiv.org/abs/1907.01218
AUTHORS:        Artem Kaznatcheev ; David A. Cohen ; Peter G. Jeavons
COMMENTS:       25 pages, 9 figures. Extended journal version; conference version appeared in CP2019
HIGHLIGHT:      Here we consider how fitness landscapes can be represented using valued constraints, and investigate what the structure of such representations reveals about the complexity of local search.

8, TITLE:       Exploring TTS without T Using Biologically/Psychologically Motivated Neural Network Modules (ZeroSpeech 2020)
http://arxiv.org/abs/2005.05487
AUTHORS:        Takashi Morita ; Hiroki Koda
COMMENTS:       Submitted to INTERSPEECH 2020
HIGHLIGHT:      In this study, we reported our exploration of Text-To-Speech without Text (TTS without T) in the Zero Resource Speech Challenge 2020, in which participants proposed an end-to-end, unsupervised system that learned speech recognition and TTS together.

9, TITLE:       Coronary Artery Segmentation in Angiographic Videos Using A 3D-2D CE-Net
http://arxiv.org/abs/2003.11851
AUTHORS:        Lu Wang ; Dong-xue Liang ; Xiao-lei Yin ; Jing Qiu ; Zhi-yun Yang ; Jun-hui Xing ; Jian-zeng Dong ; Zhao-yuan Ma
HIGHLIGHT:      This article proposes a new video segmentation framework that can extract the clearest and most comprehensive coronary angiography images from a video sequence, thereby helping physicians to better observe the condition of blood vessels.

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

11, TITLE:      Prescribing Deep Attentive Score Prediction Attracts Improved Student Engagement
http://arxiv.org/abs/2005.05021
AUTHORS:        Youngnam Lee ; Byungsoo Kim ; Dongmin Shin ; JungHoon Kim ; Jineon Baek ; Jinhwan Lee ; Youngduck Choi
HIGHLIGHT:      In this paper, we demonstrate that the accuracy of the score prediction model deployed in a real-world setting significantly impacts user engagement by providing empirical evidence.

12, TITLE:      Learning from Rules Generalizing Labeled Exemplars
http://arxiv.org/abs/2004.06025
AUTHORS:        Abhijeet Awasthi ; Sabyasachi Ghosh ; Rasna Goyal ; Sunita Sarawagi
COMMENTS:       ICLR 2020 (Spotlight)
HIGHLIGHT:      We propose a rule-exemplar method for collecting human supervision to combine the efficiency of rules with the quality of instance labels.

13, TITLE:      Towards Learning Instantiated Logical Rules from Knowledge Graphs
http://arxiv.org/abs/2003.06071
AUTHORS:        Yulong Gu ; Yu Guan ; Paolo Missier
HIGHLIGHT:      In this work, we present GPFL, a probabilistic rule learner optimized to mine instantiated first-order logic rules from KGs.

14, TITLE:      Adversarial Perturbations Fool Deepfake Detectors
http://arxiv.org/abs/2003.10596
AUTHORS:        Apurva Gandhi ; Shomik Jain
COMMENTS:       To appear in the proceedings of the International Joint Conference on Neural Networks (IJCNN 2020)
HIGHLIGHT:      This work uses adversarial perturbations to enhance deepfake images and fool common deepfake detectors.

15, TITLE:      Compressing BERT: Studying the Effects of Weight Pruning on Transfer Learning
http://arxiv.org/abs/2002.08307
AUTHORS:        Mitchell A. Gordon ; Kevin Duh ; Nicholas Andrews
COMMENTS:       Accepted to Rep4NLP 2020 Workshop at ACL 2020 Conference
HIGHLIGHT:      Pre-trained universal feature extractors, such as BERT for natural language processing and VGG for computer vision, have become effective methods for improving deep learning models without requiring more labeled data.

16, TITLE:      Adapting Behaviour via Intrinsic Reward: A Survey and Empirical Study
http://arxiv.org/abs/1906.07865
AUTHORS:        Cam Linke ; Nadia M. Ady ; Martha White ; Thomas Degris ; Adam White
HIGHLIGHT:      In this paper, we investigate and compare different intrinsic reward mechanisms in a new bandit-like parallel-learning testbed.

17, TITLE:      Knowledge-Based Matching of $n$-ary Tuples
http://arxiv.org/abs/2002.08103
AUTHORS:        Pierre Monnin ; Miguel Couceiro ; Amedeo Napoli ; Adrien Coulet
HIGHLIGHT:      In this paper, we focus on matching n-ary tuples in a knowledge base with a rule-based methodology.

18, TITLE:      Minimum Latency Training Strategies for Streaming Sequence-to-Sequence ASR
http://arxiv.org/abs/2004.05009
AUTHORS:        Hirofumi Inaguma ; Yashesh Gaur ; Liang Lu ; Jinyu Li ; Yifan Gong
COMMENTS:       Accepted at IEEE ICASSP 2020
HIGHLIGHT:      Recently, a few novel streaming attention-based sequence-to-sequence (S2S) models have been proposed to perform online speech recognition with linear-time decoding complexity.

19, TITLE:      Unsupervised Eyeglasses Removal in the Wild
http://arxiv.org/abs/1909.06989
AUTHORS:        Bingwen Hu ; Zhedong Zheng ; Ping Liu ; Wankou Yang ; Mingwu Ren
HIGHLIGHT:      To address the limitation, we propose a unified eyeglass removal model called Eyeglasses Removal Generative Adversarial Network (ERGAN), which could handle different types of glasses in the wild.

20, TITLE:      Enriching lexical-based approach with external knowledge for Vietnamese multiple-choice reading comprehension
http://arxiv.org/abs/2001.05687
AUTHORS:        Kiet Van Nguyen ; Khiem Vinh Tran ; Son T. Luu ; Anh Gia-Tuan Nguyen ; Ngan Luu-Thuy Nguyen
HIGHLIGHT:      This article proposes the lexical-based reading comprehension approach utilizing semantic similarity measurement and external knowledge sources to analyze questions and extract answers from reading texts in Vietnamese.

21, TITLE:      Feature Augmentation Improves Anomalous Change Detection for Human Activity Identification in Synthetic Aperture Radar Imagery
http://arxiv.org/abs/1912.03539
AUTHORS:        Hannah J. Murphy ; Christopher X. Ren ; Matthew T. Calef
HIGHLIGHT:      In this paper we evaluate methods to improve the performance of ACD in detecting human activity in SAR imagery using outdoor music festivals as a target.

22, TITLE:      AVID: Learning Multi-Stage Tasks via Pixel-Level Translation of Human Videos
http://arxiv.org/abs/1912.04443
AUTHORS:        Laura Smith ; Nikita Dhawan ; Marvin Zhang ; Pieter Abbeel ; Sergey Levine
COMMENTS:       To appear at Robotics: Science and Systems (RSS), 2020. Project website: https://sites.google.com/view/rss20avid
HIGHLIGHT:      In this paper, we study how these challenges can be alleviated with an automated robotic learning framework, in which multi-stage tasks are defined simply by providing videos of a human demonstrator and then learned autonomously by the robot from raw image observations.

23, TITLE:      An Annotated Dataset of Coreference in English Literature
http://arxiv.org/abs/1912.01140
AUTHORS:        David Bamman ; Olivia Lewke ; Anya Mansoor
HIGHLIGHT:      We present in this work a new dataset of coreference annotations for works of literature in English, covering 29,103 mentions in 210,532 tokens from 100 works of fiction.

24, TITLE:      Negated and Misprimed Probes for Pretrained Language Models: Birds Can Talk, But Cannot Fly
http://arxiv.org/abs/1911.03343
AUTHORS:        Nora Kassner ; Hinrich Schütze
COMMENTS:       ACL 2020
HIGHLIGHT:      Building on Petroni et al. (2019), we propose two new probing tasks analyzing factual knowledge stored in Pretrained Language Models (PLMs).

25, TITLE:      A Generating-Extension-Generator for Machine Code
http://arxiv.org/abs/2005.06645
AUTHORS:        Michael Vaughn ; Thomas Reps
COMMENTS:       21 pages, 8 Figures Fixed inclusion of LaTeX macro in plaintext abstract
HIGHLIGHT:      This paper presents a new approach to state management in a program specializer.

26, TITLE:      Mix and match networks: cross-modal alignment for zero-pair image-to-image translation
http://arxiv.org/abs/1903.04294
AUTHORS:        Yaxing Wang ; Luis Herranz ; Joost van de Weijer
COMMENTS:       Accepted by IJCV
HIGHLIGHT:      We propose mix and match networks, an approach where multiple encoders and decoders are aligned in such a way that the desired translation can be obtained by simply cascading the source encoder and the target decoder, even when they have not interacted during the training stage (i.e. unseen).

27, TITLE:      Face Behavior a la carte: Expressions, Affect and Action Units in a Single Network
http://arxiv.org/abs/1910.11111
AUTHORS:        Dimitrios Kollias ; Viktoriia Sharmanska ; Stefanos Zafeiriou
HIGHLIGHT:      We present the first and the largest study of all facial behaviour tasks learned jointly in a single multi-task, multi-domain and multi-label network, which we call FaceBehaviorNet.

28, TITLE:      Localization of Fake News Detection via Multitask Transfer Learning
http://arxiv.org/abs/1910.09295
AUTHORS:        Jan Christian Blaise Cruz ; Julianne Agatha Tan ; Charibeth Cheng
COMMENTS:       Published in the LREC 2020 Proceedings. Models and data available at https://github.com/jcblaisecruz02/Tagalog-fake-news
HIGHLIGHT:      In this work, we make two main contributions: First, we alleviate resource scarcity by constructing the first expertly-curated benchmark dataset for fake news detection in Filipino, which we call "Fake News Filipino."

29, TITLE:      Parallel Iterative Edit Models for Local Sequence Transduction
http://arxiv.org/abs/1910.02893
AUTHORS:        Abhijeet Awasthi ; Sunita Sarawagi ; Rasna Goyal ; Sabyasachi Ghosh ; Vihari Piratla
COMMENTS:       Accepted at EMNLP-IJCNLP 2019
HIGHLIGHT:      We present a Parallel Iterative Edit (PIE) model for the problem of local sequence transduction arising in tasks like Grammatical error correction (GEC).

30, TITLE:      A Multi-view Perspective of Self-supervised Learning
http://arxiv.org/abs/2003.00877
AUTHORS:        Chuanxing Geng ; Zhenghao Tan ; Songcan Chen
HIGHLIGHT:      In this paper, we borrow a multi-view perspective to decouple a class of popular pretext tasks into a combination of view data augmentation (VDA) and view label classification (VLC), where we attempt to explore the essence of such pretext task while providing some insights into its design.

31, TITLE:      "None of the Above":Measure Uncertainty in Dialog Response Retrieval
http://arxiv.org/abs/2004.01926
AUTHORS:        Yulan Feng ; Shikib Mehri ; Maxine Eskenazi ; Tiancheng Zhao
COMMENTS:       Accepted to ACL 2020 as short paper
HIGHLIGHT:      This paper discusses the importance of uncovering uncertainty in end-to-end dialog tasks, and presents our experimental results on uncertainty classification on the Ubuntu Dialog Corpus.

32, TITLE:      Universal Litmus Patterns: Revealing Backdoor Attacks in CNNs
http://arxiv.org/abs/1906.10842
AUTHORS:        Soheil Kolouri ; Aniruddha Saha ; Hamed Pirsiavash ; Heiko Hoffmann
COMMENTS:       CVPR 2020 Oral
HIGHLIGHT:      In this paper, we introduce a benchmark technique for detecting backdoor attacks (aka Trojan attacks) on deep convolutional neural networks (CNNs).

33, TITLE:      Analyzing Knowledge Graph Embedding Methods from a Multi-Embedding Interaction Perspective
http://arxiv.org/abs/1903.11406
AUTHORS:        Hung Nghiep Tran ; Atsuhiro Takasu
COMMENTS:       DSI4 at EDBT/ICDT 2019. Source code is available on github at https://github.com/tranhungnghiep/AnalyzingKGEmbeddings
HIGHLIGHT:      In this paper, we propose a multi-embedding interaction mechanism as a new approach to uniting and generalizing these models.

34, TITLE:      Robot Navigation in Unseen Spaces using an Abstract Map
http://arxiv.org/abs/2001.11684
AUTHORS:        Ben Talbot ; Feras Dayoub ; Peter Corke ; Gordon Wyeth
COMMENTS:       15 pages, published in IEEE Transactions on Cognitive and Developmental Systems (http://doi.org/10.1109/TCDS.2020.2993855), see https://btalb.github.io/abstract_map/ for access to software
HIGHLIGHT:      We present a robot navigation system that uses the same symbolic spatial information employed by humans to purposefully navigate in unseen built environments with a level of performance comparable to humans.

35, TITLE:      Improve CAM with Auto-adapted Segmentationand Co-supervised Augmentation
http://arxiv.org/abs/1911.07160
AUTHORS:        Guofeng Cui ; Ziyi Kou ; Shaojie Wang ; Wentian Zhao ; Chenliang Xu
COMMENTS:       14 pages, 4 figures
HIGHLIGHT:      In this paper, we propose aconfidence segmentation (ConfSeg) module that builds confidence scorefor each pixel in CAM without introducing additional hyper-parameters.

36, TITLE:      Plague Dot Text: Text mining and annotation of outbreak reports of the Third Plague Pandemic (1894-1952)
http://arxiv.org/abs/2002.01415
AUTHORS:        Arlene Casey ; Mike Bennett ; Richard Tobin ; Claire Grover ; Iona Walker ; Lukas Engelmann ; Beatrice Alex
HIGHLIGHT:      In this paper we discuss the progress of our ongoing exploratory project, how we enhance optical character recognition (OCR) methods to improve text capture, our approach to structure the narratives and identify relevant entities in the reports.

37, TITLE:      TX-Ray: Quantifying and Explaining Model-Knowledge Transfer in (Un-)Supervised NLP
http://arxiv.org/abs/1912.00982
AUTHORS:        Nils Rethmeier ; Vageesh Kumar Saxena ; Isabelle Augenstein
HIGHLIGHT:      Thus, for TX-Ray, we modify the established computer vision explainability principle of 'visualizing preferred inputs of neurons' to make it usable transfer analysis and NLP.

38, TITLE:      REFINED (REpresentation of Features as Images with NEighborhood Dependencies): A novel feature representation for Convolutional Neural Networks
http://arxiv.org/abs/1912.05687
AUTHORS:        Omid Bazgir ; Ruibo Zhang ; Saugato Rahman Dhruba ; Raziur Rahman ; Souparno Ghosh ; Ranadip Pal
HIGHLIGHT:      We present a novel approach for representation of high dimensional feature vector in a compact image form, termed REFINED (REpresentation of Features as Images with NEighborhood Dependencies), that is conducible for convolutional neural network based deep learning.

39, TITLE:      YELM: End-to-End Contextualized Entity Linking
http://arxiv.org/abs/1911.03834
AUTHORS:        Haotian Chen ; Sahil Wadhwa ; Xi David Li ; Andrej Zukov-Gregoric
COMMENTS:       5 pages, 2 tables
HIGHLIGHT:      We propose yet another entity linking model (YELM) which links words to entities instead of spans.

40, TITLE:      Towards Conversational Recommendation over Multi-Type Dialogs
http://arxiv.org/abs/2005.03954
AUTHORS:        Zeming Liu ; Haifeng Wang ; Zheng-Yu Niu ; Hua Wu ; Wanxiang Che ; Ting Liu
COMMENTS:       Accepted by ACL 2020
HIGHLIGHT:      We focus on the study of conversational recommendation in the context of multi-type dialogs, where the bots can proactively and naturally lead a conversation from a non-recommendation dialog (e.g., QA) to a recommendation dialog, taking into account user's interests and feedback.

41, TITLE:      Bayesian Bits: Unifying Quantization and Pruning
http://arxiv.org/abs/2005.07093
AUTHORS:        Mart van Baalen ; Christos Louizos ; Markus Nagel ; Rana Ali Amjad ; Ying Wang ; Tijmen Blankevoort ; Max Welling
HIGHLIGHT:      We introduce Bayesian Bits, a practical method for joint mixed precision quantization and pruning through gradient based optimization.

42, TITLE:      Learning Scalable Multi-Agent Coordination by Spatial Differential for Traffic Signal Control
http://arxiv.org/abs/2002.11874
AUTHORS:        Junjia Liu ; Huimin Zhang ; Zhuang Fu ; Yao Wang
COMMENTS:       13 pages, 13 figures
HIGHLIGHT:      In this paper, we design a multi-agent coordination framework based on Deep Reinforcement Learning method for traffic signal control, defined as gamma-Reward that includes both original gamma-Reward} and gamma-Attention-Reward.

43, TITLE:      Representations, Metrics and Statistics For Shape Analysis of Elastic Graphs
http://arxiv.org/abs/2003.00287
AUTHORS:        Xiaoyang Guo ; Anuj Srivastava
COMMENTS:       Visualization improved
HIGHLIGHT:      This paper introduces a far-reaching geometric approach for analyzing shapes of graphical objects, such as road networks, blood vessels, brain fiber tracts, etc.

44, TITLE:      TextSLAM: Visual SLAM with Planar Text Features
http://arxiv.org/abs/1912.05002
AUTHORS:        Boying Li ; Danping Zou ; Daniele Sartori ; Ling Pei ; Wenxian Yu
COMMENTS:       Accepted by ICRA2020
HIGHLIGHT:      We propose to integrate text objects in man-made scenes tightly into the visual SLAM pipeline.

45, TITLE:      Cycle-Consistent Adversarial Networks for Realistic Pervasive Change Generation in Remote Sensing Imagery
http://arxiv.org/abs/1911.12546
AUTHORS:        Christopher X. Ren ; Amanda Ziemann ; Alice M. S. Durieux ; James Theiler
HIGHLIGHT:      This paper introduces a new method of generating realistic pervasive changes in the context of evaluating the effectiveness of change detection algorithms in controlled settings.

46, TITLE:      Representation Learning for Discovering Phonemic Tone Contours
http://arxiv.org/abs/1910.08987
AUTHORS:        Bai Li ; Jing Yi Xie ; Frank Rudzicz
COMMENTS:       Accepted by SIGMORPHON 2020: 17th SIGMORPHON Workshop on Computational Research in Phonetics, Phonology, and Morphology
HIGHLIGHT:      In this work, we use unsupervised representation learning to identify probable clusters of syllables that share the same phonemic tone.

47, TITLE:      First return then explore
http://arxiv.org/abs/2004.12919
AUTHORS:        Adrien Ecoffet ; Joost Huizinga ; Joel Lehman ; Kenneth O. Stanley ; Jeff Clune
COMMENTS:       45 pages, 13 figures, 4 tables; reorganized sections and modified SI text extensively
HIGHLIGHT:      We introduce Go-Explore, a family of algorithms that addresses these two challenges directly through the simple principles of explicitly remembering promising states and first returning to such states before exploring.

48, TITLE:      On-device Filtering of Social Media Images for Efficient Storage
http://arxiv.org/abs/2004.02489
AUTHORS:        Dhruval Jain ; DP Mohanty ; Sanjeev Roy ; Naresh Purre ; Sukumar Moharana
HIGHLIGHT:      To address this, we propose a novel method based on Convolutional Neural Networks (CNNs) for the on-device filtering of social media images by classifying these synthetic images and allowing the user to delete them in one go.

49, TITLE:      Verification of Deep Convolutional Neural Networks Using ImageStars
http://arxiv.org/abs/2004.05511
AUTHORS:        Hoang-Dung Tran ; Stanley Bak ; Weiming Xiang ; Taylor T. Johnson
HIGHLIGHT:      In this paper, we describe a set-based framework that successfully deals with real-world CNNs, such as VGG16 and VGG19, that have high accuracy on ImageNet.

50, TITLE:      The Complexity of Helly-$B_{1}$ EPG Graph Recognition
http://arxiv.org/abs/1906.11185
AUTHORS:        Claudson F. Bornstein ; Martin Charles Golumbic ; Tanilson D. Santos ; Uéverton S. Souza ; Jayme L. Szwarcfiter
HIGHLIGHT:      In this paper, we show that given a graph $G$ and an integer $k$, the problem of determining whether $G$ admits a $B_k$-EPG representation whose edge-intersections of paths satisfy the Helly property, so-called Helly-$B_k$-EPG representation, is in NP, for every $k$ bounded by a polynomial function of $|V(G)|$.

51, TITLE:      Quickest Change Detection of Time Inconsistent Anticipatory Agents. Behavioral Economics Models in Signal Processing
http://arxiv.org/abs/2003.10910
AUTHORS:        Vikram Krishnamurthy
HIGHLIGHT:      We consider the interaction between anticipatory agents and statistical detection.

52, TITLE:      HTMLPhish: Enabling Phishing Web Page Detection by Applying Deep Learning Techniques on HTML Analysis
http://arxiv.org/abs/1909.01135
AUTHORS:        Chidimma Opara ; Bo Wei ; Yingke Chen
HIGHLIGHT:      In this paper, we propose HTMLPhish, a deep learning based data-driven end-to-end automatic phishing web page classification approach.

53, TITLE:      Understanding Dynamic Scenes using Graph Convolution Networks
http://arxiv.org/abs/2005.04437
AUTHORS:        Sravan Mylavarapu ; Mahtab Sandhu ; Priyesh Vijayan ; K Madhava Krishna ; Balaraman Ravindran ; Anoop Namboodiri
COMMENTS:       Under Review
HIGHLIGHT:      We present a novel Multi Relational Graph Convolutional Network (MRGCN) to model on-road vehicle behaviours from a sequence of temporally ordered frames as grabbed by a moving monocular camera.