2020.06.03.txt

==========New Papers==========
1, TITLE:       Web Document Categorization Using Naive Bayes Classifier and Latent Semantic Analysis
http://arxiv.org/abs/2006.01715
AUTHORS:        Alireza Saleh Sedghpour ; Mohammad Reza Saleh Sedghpour
HIGHLIGHT:      In order to solve the problem, we proposed a method for web document classification that uses LSA to increase similarity of documents under the same class and improve the classification precision.

2, TITLE:       Studying The Effect of MIL Pooling Filters on MIL Tasks
http://arxiv.org/abs/2006.01561
AUTHORS:        Mustafa Umit Oner ; Jared Marc Song Kye-Jet ; Hwee Kuan Lee ; Wing-Kin Sung
COMMENTS:       16 pages
HIGHLIGHT:      In this paper, we study the effect of different MIL pooling filters on the performance of MIL models in real world MIL tasks.

3, TITLE:       Two-hand Global 3D Pose Estimation Using Monocular RGB
http://arxiv.org/abs/2006.01320
AUTHORS:        Fanqing Lin ; Connor Wilhelm ; Tony Martinez
HIGHLIGHT:      We propose a novel multi-stage convolutional neural network based pipeline that accurately segments and locates the hands despite occlusion between two hands and complex background noise and estimates the 2D and 3D canonical joint locations without any depth information. We demonstrate that our system outperforms previous works on 3D canonical hand pose estimation benchmark datasets with RGB-only information.

4, TITLE:       Multi-view Deep Features for Robust Facial Kinship Verification
http://arxiv.org/abs/2006.01315
AUTHORS:        Oualid Laiadi ; Abdelmalik Ouamane ; Abdelhamid Benakcha ; Abdelmalik Taleb-Ahmed ; Abdenour Hadid
COMMENTS:       Will appear as part of RFIW2020 in the Proceedings of 2020 International Conference on Automatic Face and Gesture Recognition (IEEE AMFG)
HIGHLIGHT:      In this paper, we proposed an effective facial features extraction model based on multi-view deep features.

5, TITLE:       Eye Movements Biometrics: A Bibliometric Analysis from 2004 to 2019
http://arxiv.org/abs/2006.01310
AUTHORS:        Antonio Ricardo Alexandre Brasil ; Jefferson Oliveira Andrade ; Karin Satie Komati
COMMENTS:       9 pages, 2 figures, journal
HIGHLIGHT:      This paper presents a comprehensive quantitative overview of the field of eye movement biometrics using a bibliometric approach.

6, TITLE:       Shapley Value as Principled Metric for Structured Network Pruning
http://arxiv.org/abs/2006.01795
AUTHORS:        Marco Ancona ; Cengiz Öztireli ; Markus Gross
HIGHLIGHT:      In this work, we show that this is not true on a low-data regime when fine-tuning is either not possible or not effective.

7, TITLE:       A Contextual Hierarchical Attention Network with Adaptive Objective for Dialogue State Tracking
http://arxiv.org/abs/2006.01554
AUTHORS:        Yong Shan ; Zekang Li ; Jinchao Zhang ; Fandong Meng ; Yang Feng ; Cheng Niu ; Jie Zhou
COMMENTS:       Accepted as a long paper at ACL 2020. Code is available at https://github.com/ictnlp/CHAN-DST
HIGHLIGHT:      In this paper, we propose to enhance the DST through employing a contextual hierarchical attention network to not only discern relevant information at both word level and turn level but also learn contextual representations.

8, TITLE:       Neural Speaker Diarization with Speaker-Wise Chain Rule
http://arxiv.org/abs/2006.01796
AUTHORS:        Yusuke Fujita ; Shinji Watanabe ; Shota Horiguchi ; Yawen Xue ; Jing Shi ; Kenji Nagamatsu
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      In this paper, we solve this fixed number of speaker issue by a novel speaker-wise conditional inference method based on the probabilistic chain rule.

9, TITLE:       Object-Independent Human-to-Robot Handovers using Real Time Robotic Vision
http://arxiv.org/abs/2006.01797
AUTHORS:        Patrick Rosenberger ; Akansel Cosgun ; Rhys Newbury ; Jun Kwan ; Valerio Ortenzi ; Peter Corke ; Manfred Grafinger
COMMENTS:       The code and videos can be found at https://patrosat.github.io/h2r_handovers/
HIGHLIGHT:      We present an approach for safe and object-independent human-to-robot handovers using real time robotic vision and manipulation.

10, TITLE:      Depth-Optimized Delay-Aware Tree (DO-DAT) for Virtual Network Function Placement
http://arxiv.org/abs/2006.01790
AUTHORS:        Dimitrios Michael Manias ; Hassan Hawilo ; Manar Jammal ; Abdallah Shami
HIGHLIGHT:      In this paper, we present a machine learning-based solution to the Virtual Network Function (VNF) placement problem.

11, TITLE:      Rethinking Empirical Evaluation of Adversarial Robustness Using First-Order Attack Methods
http://arxiv.org/abs/2006.01304
AUTHORS:        Kyungmi Lee ; Anantha P. Chandrakasan
HIGHLIGHT:      For each case, we propose compensation methods that either address sources of inaccurate gradient computation, such as numerical instability near zero and non-differentiability, or reduce the total number of back-propagations for iterative attacks by approximating second-order information.

12, TITLE:      Coordinating Multiagent Industrial Symbiosis
http://arxiv.org/abs/2006.01784
AUTHORS:        Vahid Yazdanpanah ; Devrim Murat Yazan ; W. Henk M. Zijm
HIGHLIGHT:      We present a formal multiagent framework for coordinating a class of collaborative industrial practices called Industrial Symbiotic Networks (ISNs) as cooperative games.

13, TITLE:      Geometric Graph Representations and Geometric Graph Convolutions for Deep Learning on Three-Dimensional (3D) Graphs
http://arxiv.org/abs/2006.01785
AUTHORS:        Daniel T. Chang
HIGHLIGHT:      To facilitate the incorporation of geometry in deep learning on 3D graphs, we define three types of geometric graph representations: positional, angle-geometric and distance-geometric.

14, TITLE:      A Novel Nudity Detection Algorithm for Web and Mobile Application Development
http://arxiv.org/abs/2006.01780
AUTHORS:        Rahat Yeasin Emon
COMMENTS:       5 pages
HIGHLIGHT:      This paper presents a runtime nudity detection method for web and mobile application development.

15, TITLE:      Effectful Programming in Declarative Languages with an Emphasis on Non-Determinism: Applications and Formal Reasoning
http://arxiv.org/abs/2006.01531
AUTHORS:        Sandra Dylus
COMMENTS:       in German, Dissertation as submitted for publication at University of Kiel
HIGHLIGHT:      The other application that we discuss in this work is a library for probabilistic programming.

16, TITLE:      WikiBERT models: deep transfer learning for many languages
http://arxiv.org/abs/2006.01538
AUTHORS:        Sampo Pyysalo ; Jenna Kanerva ; Antti Virtanen ; Filip Ginter
COMMENTS:       7 pages, 1 figure
HIGHLIGHT:      In this paper, we introduce a simple, fully automated pipeline for creating language-specific BERT models from Wikipedia data and introduce 42 new such models, most for languages up to now lacking dedicated deep neural language models.

17, TITLE:      What's Sex Got To Do With Machine Learning
http://arxiv.org/abs/2006.01770
AUTHORS:        Lily Hu ; Issa Kohler-Hausmann
COMMENTS:       11 pages, 5 figures, ACM Conference on Fairness, Accountability, and Transparency
HIGHLIGHT:      Causal models of sex propose two substantive claims: 1) There exists a feature, sex-on-its-own, that is an inherent trait of an individual that causally brings about social phenomena external to it in the world; and 2) the relations between sex and its effects can be modified in whichever ways and the former feature would still retain the meaning that sex has in our world.

18, TITLE:      AnalogNet: Convolutional Neural Network Inference on Analog Focal Plane Sensor Processors
http://arxiv.org/abs/2006.01765
AUTHORS:        Matthew Z. Wong ; Benoit Guillard ; Riku Murai ; Sajad Saeedi ; Paul H. J. Kelly
COMMENTS:       8 pages, 7 figures
HIGHLIGHT:      In this work, we show how to convert standard CNNs to FPSP code, and demonstrate a method of training networks to increase their robustness to analog computation errors.

19, TITLE:      Question Answering on Scholarly Knowledge Graphs
http://arxiv.org/abs/2006.01527
AUTHORS:        Mohamad Yaser Jaradeh ; Markus Stocker ; Sören Auer
COMMENTS:       Pre-print for TPDL2020 accepted full paper, 14 pages
HIGHLIGHT:      We present JarvisQA, a BERT based system to answer questions on tabular views of scholarly knowledge graphs. Furthermore, we present a preliminary dataset of related tables and a corresponding set of natural language questions.

20, TITLE:      SAT Heritage: a community-driven effort for archiving, building and running more than thousand SAT solvers
http://arxiv.org/abs/2006.01503
AUTHORS:        Gilles Audemard ; Loïc Paulevé ; Laurent Simon
HIGHLIGHT:      We propose to initiate a community-driven effort to archive and to allow easy compilation and running of all SAT solvers that have been released so far.

21, TITLE:      Context-based Transformer Models for Answer Sentence Selection
http://arxiv.org/abs/2006.01285
AUTHORS:        Ivano Lauriola ; Alessandro Moschitti
HIGHLIGHT:      In this paper, we analyze the role of the contextual information in the sentence selection task, proposing a Transformer based architecture that leverages two types of contexts, local and global.

22, TITLE:      Fusion of Real Time Thermal Image and 1D/2D/3D Depth Laser Readings for Remote Thermal Sensing in Industrial Plants by Means of UAVs and/or Robots
http://arxiv.org/abs/2006.01286
AUTHORS:        Corneliu Arsene
HIGHLIGHT:      This paper presents fast procedures for thermal infrared remote sensing in dark, GPS-denied environments, such as those found in industrial plants such as in High-Voltage Direct Current (HVDC) converter stations.

23, TITLE:      Shapley-based explainability on the data manifold
http://arxiv.org/abs/2006.01272
AUTHORS:        Christopher Frye ; Damien de Mijolla ; Laurence Cowton ; Megan Stanley ; Ilya Feige
COMMENTS:       8 pages, 5 figures, 2 appendices
HIGHLIGHT:      In this work, we articulate the dangers of this assumption and introduce two solutions for computing Shapley explanations that respect the data manifold.

24, TITLE:      A comparative study of 2D image segmentation algorithms for traumatic brain lesions using CT data from the ProTECTIII multicenter clinical trial
http://arxiv.org/abs/2006.01263
AUTHORS:        Shruti Jadon ; Owen P. Leary ; Ian Pan ; Tyler J. Harder ; David W. Wright ; Lisa H. Merck ; Derek L. Merck
COMMENTS:       9 pages, 3 figures, 3 tables
HIGHLIGHT:      In this research, we have experimented with multiple available deep learning architectures to segment different phenotypes of hemorrhagic lesions found after moderate to severe traumatic brain injury (TBI).

25, TITLE:      Walking through Doors is Hard, even without Staircases: Proving PSPACE-hardness via Planar Assemblies of Door Gadgets
http://arxiv.org/abs/2006.01256
AUTHORS:        Joshua Ani ; Jeffrey Bosboom ; Erik D. Demaine ; Yevhenii Diomidov ; Dylan Hendrickson ; Jayson Lynch
COMMENTS:       Accepted to FUN2020, 35 pages, 41 figures
HIGHLIGHT:      We prove that it is PSPACE-complete to decide whether an agent can move from one location to another through a planar assembly of such door gadgets, removing the traditional need for crossover gadgets and thereby simplifying past PSPACE-hardness proofs of Lemmings and Nintendo games Super Mario Bros., Legend of Zelda, and Donkey Kong Country.

26, TITLE:      The Fine-Grained Complexity of Andersen's Pointer Analysis
http://arxiv.org/abs/2006.01491
AUTHORS:        Andreas Pavlogiannis
HIGHLIGHT:      In this work we draw a rich fine-grained complexity landscape of APA, and present upper and lower bounds.

27, TITLE:      An Effectiveness Metric for Ordinal Classification: Formal Properties and Experimental Results
http://arxiv.org/abs/2006.01245
AUTHORS:        Enrique Amigó ; Julio Gonzalo ; Stefano Mizzaro ; Jorge Carrillo-de-Albornoz
COMMENTS:       To appear in Proceedings of ACL 2020
HIGHLIGHT:      In this paper we propose a new metric for Ordinal Classification, Closeness Evaluation Measure, that is rooted on Measurement Theory and Information Theory.

28, TITLE:      Extending the Multiple Traveling Salesman Problem for Scheduling a Fleet of Drones Performing Monitoring Missions
http://arxiv.org/abs/2006.01473
AUTHORS:        Emmanouil Rigas ; Panayiotis Kolios ; Georgios Ellinas
COMMENTS:       To appear in the 23rd IEEE International Conference on Intelligent Transportation Systems
HIGHLIGHT:      In this paper we schedule the travel path of a set of drones across a graph where the nodes need to be visited multiple times at pre-defined points in time.

29, TITLE:      BWCNN: Blink to Word, a Real-Time Convolutional Neural Network Approach
http://arxiv.org/abs/2006.01232
AUTHORS:        Albara Ah Ramli ; Rex Liu ; Rahul Krishnamoorthy ; Vishal I B ; Xiaoxiao Wang ; Ilias Tagkopoulos ; Xin Liu
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      Here, We present an Artificial Intelligence (AI) system that uses eye-blinks to communicate with the outside world, running on real-time Internet-of-Things (IoT) devices.

30, TITLE:      Channel Attention based Iterative Residual Learning for Depth Map Super-Resolution
http://arxiv.org/abs/2006.01469
AUTHORS:        Xibin Song ; Yuchao Dai ; Dingfu Zhou ; Liu Liu ; Wei Li ; Hongdng Li ; Ruigang Yang
COMMENTS:       accepted by Conference on Computer Vision and Pattern Recognition 2020
HIGHLIGHT:      In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks.

31, TITLE:      An embedded system for the automated generation of labeled plant images to enable machine learning applications in agriculture
http://arxiv.org/abs/2006.01228
AUTHORS:        Michael A. Beck ; Chen-Yi Liu ; Christopher P. Bidinosti ; Christopher J. Henry ; Cara M. Godee ; Manisha Ajmani
COMMENTS:       35 pages, 8 figures, Preprint submitted to PLoS One
HIGHLIGHT:      To tackle this problem, we have developed an embedded robotic system to automatically generate and label large datasets of plant images for ML applications in agriculture. To demonstrate the capabilities of our system, we generated a dataset of over 34,000 labeled images, with which we trained an ML-model to distinguish grasses from non-grasses in test data from a variety of sources.

32, TITLE:      BERT-based Ensembles for Modeling Disclosure and Support in Conversational Social Media Text
http://arxiv.org/abs/2006.01222
AUTHORS:        Tanvi Dadu ; Kartikey Pant ; Radhika Mamidi
COMMENTS:       Accepted at the Affective Content workshop held at AAAI 2020 as the Best System Paper
HIGHLIGHT:      In this paper, we introduce a predictive ensemble model exploiting the finetuned contextualized word embeddings, RoBERTa and ALBERT.

33, TITLE:      Situated and Interactive Multimodal Conversations
http://arxiv.org/abs/2006.01460
AUTHORS:        Seungwhan Moon ; Satwik Kottur ; Paul A. Crook ; Ankita De ; Shivani Poddar ; Theodore Levin ; David Whitney ; Daniel Difranco ; Ahmad Beirami ; Eunjoon Cho ; Rajen Subba ; Alborz Geramifard
COMMENTS:       12 pages, 5 figures, 9 tables, 3 supplementary pages
HIGHLIGHT:      We introduce Situated Interactive MultiModal Conversations (SIMMC) as a new direction aimed at training agents that take multimodal actions grounded in a co-evolving multimodal input context in addition to the dialog history. We provide two SIMMC datasets totalling ~13K human-human dialogs (~169K utterances) using a multimodal Wizard-of-Oz (WoZ) setup, on two shopping domains: (a) furniture (grounded in a shared virtual environment) and, (b) fashion (grounded in an evolving set of images).

34, TITLE:      Perturbation Analysis of Gradient-based Adversarial Attacks
http://arxiv.org/abs/2006.01456
AUTHORS:        Utku Ozbulak ; Manvel Gasparyan ; Wesley De Neve ; Arnout Van Messem
COMMENTS:       Accepted for publication in Pattern Recognition Letters, 2020
HIGHLIGHT:      In this paper, we investigate the objective functions of three popular methods for adversarial example generation: the L-BFGS attack, the Iterative Fast Gradient Sign attack, and Carlini & Wagner's attack (CW).

35, TITLE:      High-quality Panorama Stitching based on Asymmetric Bidirectional Optical Flow
http://arxiv.org/abs/2006.01201
AUTHORS:        Mingyuan Meng ; Shaojun Liu
COMMENTS:       5 pages, 4 figures, accepted at the 5th International Conference on Computational Intelligence and Applications (ICCIA 2020)
HIGHLIGHT:      In this paper, we propose a panorama stitching algorithm based on asymmetric bidirectional optical flow.

36, TITLE:      Descriptor Revision for Conditionals: Literal Descriptors and Conditional Preservation
http://arxiv.org/abs/2006.01444
AUTHORS:        Kai Sauerwald ; Jonas Haldimann ; Martin von Berg ; Christoph Beierle
HIGHLIGHT:      In this article, we investigate the realisation of descriptor revision for a conditional logic while restricting descriptors to the conjunction of literal descriptors.

37, TITLE:      Hybrid Improved Document-level Embedding (HIDE)
http://arxiv.org/abs/2006.01203
AUTHORS:        Satanik Mitra ; Mamata Jenamani
HIGHLIGHT:      In this work we propose HIDE a Hybrid Improved Document level Embedding which incorporates domain information, parts of speech information and sentiment information into existing word embeddings such as GloVe and Word2Vec.

38, TITLE:      SeqXFilter: A Memory-efficient Denoising Filter for Dynamic Vision Sensors
http://arxiv.org/abs/2006.01687
AUTHORS:        Shasha Guo ; Lei Wang ; Xiaofan Chen ; Limeng Zhang ; Ziyang Kang ; Weixia Xu
HIGHLIGHT:      We propose \emph{SeqXFilter}, a spatio-temporal correlation filter with only a past event window that has an O(1) space complexity and has simple computations.

39, TITLE:      NEMA: Automatic Integration of Large Network Management Databases
http://arxiv.org/abs/2006.01294
AUTHORS:        Fubao Wu ; Han Hee Song ; Jiangtao Yin ; Lixin Gao ; Mario Baldi ; Narendra Anand
COMMENTS:       14 pages, 13 Figures, 7 tables
HIGHLIGHT:      In this paper, we develop a matching technique for large NEtwork MAnagement databases (NEMA) deploying instance-level matching for effective data integration and connection.

40, TITLE:      Automatized Evaluation of Formalization Exercises in Mathematics
http://arxiv.org/abs/2006.01800
AUTHORS:        Merlin Carl
HIGHLIGHT:      We describe two systems for supporting beginner students in acquiring basic skills in expressing statements in the formalism of first-order predicate logic; the first, called "math dictations", presents users with the task of formalizing a given natural-language sentence, while the second, called "Game of Def", challenges users to give a formal description of a set of a geometric pattern displayed to them.

41, TITLE:      Fast and accurate aberration estimation from 3D bead images using convolutional neural networks
http://arxiv.org/abs/2006.01804
AUTHORS:        Debayan Saha ; Uwe Schmidt ; Qinrong Zhang ; Aurelien Barbotin ; Qi Hu ; Na Ji ; Martin J. Booth ; Martin Weigert ; Eugene W. Myers
HIGHLIGHT:      Here we describe a method (PHASENET) for fast and accurate aberration measurement from experimentally acquired 3D bead images using convolutional neural networks.

42, TITLE:      Neural Power Units
http://arxiv.org/abs/2006.01681
AUTHORS:        Niklas Heim ; Tomáš Pevný ; Václav Šmídl
HIGHLIGHT:      We introduce the Neural Power Unit (NPU) that operates on the full domain of real numbers and is capable of learning arbitrary power functions in a single layer.

43, TITLE:      CT-based COVID-19 Triage: Deep Multitask Learning Improves Joint Identification and Severity Quantification
http://arxiv.org/abs/2006.01441
AUTHORS:        Mikhail Goncharov ; Maxim Pisov ; Alexey Shevtsov ; Boris Shirokikh ; Anvar Kurmukov ; Ivan Blokhin ; Valeria Chernina ; Alexander Solovev ; Victor Gombolevskiy ; Sergey Morozov ; Mikhail Belyaev
HIGHLIGHT:      To consolidate both triage approaches, we employ a multitask learning and propose a convolutional neural network to combine all available labels within a single model.

44, TITLE:      Recapture as You Want
http://arxiv.org/abs/2006.01435
AUTHORS:        Chen Gao ; Si Liu ; Ran He ; Shuicheng Yan ; Bo Li
COMMENTS:       14 pages
HIGHLIGHT:      In this paper, we present a portrait recapture method enabling users to easily edit their portrait to desired posture/view, body figure and clothing style, which are very challenging to achieve since it requires to simultaneously perform non-rigid deformation of human body, invisible body-parts reasoning and semantic-aware editing.

45, TITLE:      Learning to do multiframe blind deconvolution unsupervisedly
http://arxiv.org/abs/2006.01438
AUTHORS:        A. Asensio Ramos
COMMENTS:       11 pages, 8 figures, submitted to A&A
HIGHLIGHT:      Our aim is to propose an unsupervised method which can then be trained simply with observations and check it with data from the FastCam instrument.

46, TITLE:      Distribution Aligned Multimodal and Multi-Domain Image Stylization
http://arxiv.org/abs/2006.01431
AUTHORS:        Minxuan Lin ; Fan Tang ; Weiming Dong ; Xiao Li ; Chongyang Ma ; Changsheng Xu
HIGHLIGHT:      In this paper, we propose a unified framework for multimodal and multi-domain style transfer with the support of both exemplar-based reference and randomly sampled guidance.

47, TITLE:      BERT Based Multilingual Machine Comprehension in English and Hindi
http://arxiv.org/abs/2006.01432
AUTHORS:        Somil Gupta ; Nilesh Khade
COMMENTS:       Submitted for review to the Special Issue on Deep Learning of ACM Transactions on Asian and Low-Resource Language Information Processing (TALLIP)
HIGHLIGHT:      We, therefore, present in this article, our experiments with m-BERT for MMC in zero-shot, mono-lingual (e.g. Hindi Question-Hindi Snippet) and cross-lingual (e.g. English QuestionHindi Snippet) fine-tune setups.

48, TITLE:      An Empirical Methodology for Detecting and Prioritizing Needs during Crisis Events
http://arxiv.org/abs/2006.01439
AUTHORS:        M. Janina Sarol ; Ly Dinh ; Rezvaneh Rezapour ; Chieh-Li Chin ; Pingjing Yang ; Jana Diesner
HIGHLIGHT:      In this study, we propose two novel methods for two distinct but related needs detection tasks: the identification of 1) a list of resources needed ranked by priority, and 2) sentences that specify who-needs-what resources.

49, TITLE:      Image Super-Resolution with Cross-Scale Non-Local Attention and Exhaustive Self-Exemplars Mining
http://arxiv.org/abs/2006.01424
AUTHORS:        Yiqun Mei ; Yuchen Fan ; Yuqian Zhou ; Lichao Huang ; Thomas S. Huang ; Humphrey Shi
COMMENTS:       CVPR2020
HIGHLIGHT:      In this paper, we propose the first Cross-Scale Non-Local (CS-NL) attention module with integration into a recurrent neural network.

50, TITLE:      Variational Inference and Learning of Piecewise-linear Dynamical Systems
http://arxiv.org/abs/2006.01668
AUTHORS:        Xavier Alameda-Pineda ; Vincent Drouard ; Radu Horaud
COMMENTS:       Submitted to IEEE Transactions on Neural Networks and Learning Systems
HIGHLIGHT:      In this paper, we propose a variational approximation of piecewise linear dynamic systems.

51, TITLE:      Monocular Human Pose Estimation: A Survey of Deep Learning-based Methods
http://arxiv.org/abs/2006.01423
AUTHORS:        Yucheng Chen ; Yingli Tian ; Mingyi He
COMMENTS:       This version corresponds to the pre-print of the paper accepted for Computer Vision and Image Understanding (CVIU)
HIGHLIGHT:      This survey extensively reviews the recent deep learning-based 2D and 3D human pose estimation methods published since 2014.

52, TITLE:      Resolving Class Imbalance in Object Detection with Weighted Cross Entropy Losses
http://arxiv.org/abs/2006.01413
AUTHORS:        Trong Huy Phan ; Kazuma Yamamoto
HIGHLIGHT:      In this paper, we propose to explore and overcome such problem by application of several weighted variants of Cross Entropy loss, for examples Balanced Cross Entropy, Focal Loss and Class-Balanced Loss Based on Effective Number of Samples to our object detector.

53, TITLE:      Enhanced Universal Dependency Parsing with Second-Order Inference and Mixture of Training Data
http://arxiv.org/abs/2006.01414
AUTHORS:        Xinyu Wang ; Yong Jiang ; Kewei Tu
COMMENTS:       IWPT 2020 shared task. After fixing the bug, our proposed parser performs better than the team that ranked \textbf{1st} in the official results
HIGHLIGHT:      This paper presents the system used in our submission to the \textit{IWPT 2020 Shared Task}.

54, TITLE:      A Layered Learning Approach to Scaling in Learning Classifier Systems for Boolean Problems
http://arxiv.org/abs/2006.01415
AUTHORS:        Isidro M. Alvarez ; Trung B. Nguyen ; Will N. Browne ; Mengjie Zhang
HIGHLIGHT:      In this paper, we propose improvements to XCSCF* to enable it to be robust across multiple problem domains.

55, TITLE:      Analyzing the Quality and Stability of a Streaming End-to-End On-Device Speech Recognizer
http://arxiv.org/abs/2006.01416
AUTHORS:        Yuan Shangguan ; Kate Knister ; Yanzhang He ; Ian McGraw ; Francoise Beaufays
COMMENTS:       Submitted to Interspeech 2020
HIGHLIGHT:      We categorize the causes of instability and explore various solutions to mitigate them in a streaming E2E ASR system. We first introduce a novel set of metrics that quantify the instability at word and segment levels.

56, TITLE:      Transfoming Multi-Concept Attention into Video Summarization
http://arxiv.org/abs/2006.01410
AUTHORS:        Yen-Ting Liu* ; Yu-Jhe Li* ; Yu-Chiang Frank Wang
HIGHLIGHT:      In this paper, we propose an novel attention-based framework for video summarization with complex video data.

57, TITLE:      Diversity Actor-Critic: Sample-Aware Entropy Regularization for Sample-Efficient Exploration
http://arxiv.org/abs/2006.01419
AUTHORS:        Seungyul Han ; Youngchul Sung
COMMENTS:       Submitted to NIPS 2020
HIGHLIGHT:      In order to take advantage of the previous sample distribution from the replay buffer for sample-efficient exploration, we propose sample-aware entropy regularization which maximizes the entropy of weighted sum of the policy action distribution and the sample action distribution from the replay buffer.

58, TITLE:      Interpretation of ResNet by Visualization of Preferred Stimulus in Receptive Fields
http://arxiv.org/abs/2006.01645
AUTHORS:        Genta Kobayashi ; Hayaru Shouno
COMMENTS:       10 pages
HIGHLIGHT:      In this research, we investigate the receptive fields of a ResNet on the classification task in ImageNet.

59, TITLE:      Exploring the role of Input and Output Layers of a Deep Neural Network in Adversarial Defense
http://arxiv.org/abs/2006.01408
AUTHORS:        Jay N. Paranjape ; Rahul Kumar Dubey ; Vijendran V Gopalan
COMMENTS:       5 pages, 7 figures, to be presented at CONF-CDS 2020
HIGHLIGHT:      In this work, we have analyzed the resistance of three different classes of fully connected dense networks against the rarely tested non-gradient based adversarial attacks.

60, TITLE:      COVIDGR dataset and COVID-SDNet methodology for predicting COVID-19 based on Chest X-Ray images
http://arxiv.org/abs/2006.01409
AUTHORS:        S. Tabik ; A. Gómez-Ríos ; J. L. Martín-Rodríguez ; I. Sevillano-García ; M. Rey-Area ; D. Charte ; E. Guirado ; J. L. Suárez ; J. Luengo ; M. A. Valero-González ; P. García-Villanova ; E. Olmedo-Sánchez ; F. Herrera
HIGHLIGHT:      COVIDGR-1.0 contains 377 positive and 377 negative PA (PosteroAnterior) CXR views and (iii) we propose COVID Smart Data based Network (COVID-SDNet) methodology for improving the generalization capacity of COVID-classification models.

61, TITLE:      Good pivots for small sparse matrices
http://arxiv.org/abs/2006.01623
AUTHORS:        Manuel Kauers ; Jakob Moosbauer
COMMENTS:       10 pages
HIGHLIGHT:      For sparse matrices up to size $8 \times 8$, we determine optimal choices for pivot selection in Gaussian elimination.

62, TITLE:      Relational Learning Analysis of Social Politics using Knowledge Graph Embedding
http://arxiv.org/abs/2006.01626
AUTHORS:        Bilal Abu-Salih ; Marwan Al-Tawil ; Ibrahim Aljarah ; Hossam Faris ; Pornpit Wongthongtham
HIGHLIGHT:      This paper presents a novel credibility domain-based KG Embedding framework.

63, TITLE:      Give Me Something to Eat: Referring Expression Comprehension with Commonsense Knowledge
http://arxiv.org/abs/2006.01629
AUTHORS:        Peng Wang ; Dongyang Liu ; Hui Li ; Qi Wu
HIGHLIGHT:      In this paper, we collect a new referring expression dataset, called KB-Ref, containing 43k expressions on 16k images.

64, TITLE:      Combining Reinforcement Learning and Constraint Programming for Combinatorial Optimization
http://arxiv.org/abs/2006.01610
AUTHORS:        Quentin Cappart ; Thierry Moisan ; Louis-Martin Rousseau ; Isabeau Prémont-Schwarz ; Andre Cire
HIGHLIGHT:      In this work, we propose a general and hybrid approach, based on DRL and CP, for solving combinatorial optimization problems.

65, TITLE:      A Multi-Task Comparator Framework for Kinship Verification
http://arxiv.org/abs/2006.01615
AUTHORS:        Stefan Hörmann ; Martin Knoche ; Gerhard Rigoll
COMMENTS:       To be published in IEEE FG 2020 - RFIW Workshop
HIGHLIGHT:      Instead of fine tuning the feature extractor network on kinship verification, we propose a comparator network to cope with this bias.

66, TITLE:      DiscSense: Automated Semantic Analysis of Discourse Markers
http://arxiv.org/abs/2006.01603
AUTHORS:        Damien Sileo ; Tim Van de Cruys ; Camille Pradel ; Philippe Muller
COMMENTS:       Accepted at LREC2020
HIGHLIGHT:      In this work, we take another perspective: using a model trained to predict discourse markers between sentence pairs, we predict plausible markers between sentence pairs with a known semantic relation (provided by existing classification datasets).

67, TITLE:      Uninitialized Capabilities
http://arxiv.org/abs/2006.01608
AUTHORS:        Sander Huyghebaert ; Thomas Van Strydonck ; Steven Keuchel ; Dominique Devriese
COMMENTS:       8 pages, 4 figures
HIGHLIGHT:      This technical report describes a new extension to capability machines.

68, TITLE:      DeepCoDA: personalized interpretability for compositional health
http://arxiv.org/abs/2006.01392
AUTHORS:        Thomas P. Quinn ; Dang Nguyen ; Santu Rana ; Sunil Gupta ; Svetha Venkatesh
COMMENTS:       To appear at ICML 2020
HIGHLIGHT:      We propose the DeepCoDA framework to extend precision health modelling to high-dimensional compositional data, and to provide personalized interpretability through patient-specific weights.

69, TITLE:      Adaptive convolutional neural networks for k-space data interpolation in fast magnetic resonance imaging
http://arxiv.org/abs/2006.01385
AUTHORS:        Tianming Du ; Honggang Zhang ; Hee Kwon Song ; Yong Fan
HIGHLIGHT:      To overcome such limitations, we develop a deep learning algorithm, referred to as adaptive convolutional neural networks for k-space data interpolation (ACNN-k-Space), which adopts a residual Encoder-Decoder network architecture to interpolate the undersampled k-space data by integrating spatially contiguous slices as multi-channel input, along with k-space data from multiple coils if available.

70, TITLE:      Embeddings of Label Components for Sequence Labeling: A Case Study of Fine-grained Named Entity Recognition
http://arxiv.org/abs/2006.01372
AUTHORS:        Takuma Kato ; Kaori Abe ; Hiroki Ouchi ; Shumpei Miyawaki ; Jun Suzuki ; Kentaro Inui
COMMENTS:       8 pages, 3 figures, 7 tables
HIGHLIGHT:      In this work, we propose to integrate label component information as embeddings into models.

71, TITLE:      A heterogeneous branch and multi-level classification network for person re-identification
http://arxiv.org/abs/2006.01367
AUTHORS:        Jiabao Wang ; Yang Li ; Yangshuo Zhang ; Zhuang Miao ; Rui Zhang
HIGHLIGHT:      In order to improve this problem, we propose a novel Heterogeneous Branch and Multi-level Classification Network (HBMCN), which is designed based on the pre-trained ResNet-50 model.

72, TITLE:      A Unified Dual-view Model for Review Summarization and Sentiment Classification with Inconsistency Loss
http://arxiv.org/abs/2006.01592
AUTHORS:        Hou Pong Chan ; Wang Chen ; Irwin King
COMMENTS:       Accepted by SIGIR 2020
HIGHLIGHT:      To effectively leverage the shared sentiment information in both review summarization and sentiment classification tasks, we propose a novel dual-view model that jointly improves the performance of these two tasks.

73, TITLE:      A Pairwise Probe for Understanding BERT Fine-Tuning on Machine Reading Comprehension
http://arxiv.org/abs/2006.01346
AUTHORS:        Jie Cai ; Zhengzhou Zhu ; Ping Nie ; Qian Liu
COMMENTS:       e.g.: 4 pages, 1 figure
HIGHLIGHT:      In this paper, inspired by the observation that most probing tasks involve identifying matched pairs of phrases (e.g. coreference requires matching an entity and a pronoun), we propose a pairwise probe to understand BERT fine-tuning on the machine reading comprehension (MRC) task.

74, TITLE:      Deep Learning in Target Space
http://arxiv.org/abs/2006.01578
AUTHORS:        Michael Fairbank ; Spyridon Samothrakis
HIGHLIGHT:      In this paper we propose to reparameterise the weights into targets for the firing strengths of the individual nodes in the network.

75, TITLE:      A Survey of Neural Networks and Formal Languages
http://arxiv.org/abs/2006.01338
AUTHORS:        Joshua Ackerman ; George Cybenko
HIGHLIGHT:      This report is a survey of the relationships between various state-of-the-art neural network architectures and formal languages as, for example, structured by the Chomsky Language Hierarchy.

76, TITLE:      CNNs on Surfaces using Rotation-Equivariant Features
http://arxiv.org/abs/2006.01570
AUTHORS:        Ruben Wiersma ; Elmar Eisemann ; Klaus Hildebrandt
COMMENTS:       12 pages, 14 figures, 5 tables, to be published in ACM ToG (SIGGRAPH 2020)
HIGHLIGHT:      We propose a network architecture for surfaces that consists of vector-valued, rotation-equivariant features.

77, TITLE:      Exploring Cross-sentence Contexts for Named Entity Recognition with BERT
http://arxiv.org/abs/2006.01563
AUTHORS:        Jouni Luoma ; Sampo Pyysalo
HIGHLIGHT:      In this paper, we present a systematic study exploring the use of cross-sentence information for NER using BERT models in five languages.

78, TITLE:      Saber Pro success prediction model using decision tree based learning
http://arxiv.org/abs/2006.01322
AUTHORS:        Gregorio Perez Bernal ; Luisa Toro Villegas ; Mauricio Toro
HIGHLIGHT:      The primary objective of this report is to determine what influences the success rates of students who have studied in Colombia, analyzing the Saber 11, the test done at the last school year, some socioeconomic aspects and comparing the Saber Pro results with the national average.

79, TITLE:      Revisiting Bounded-Suboptimal Safe Interval Path Planning
http://arxiv.org/abs/2006.01195
AUTHORS:        Konstantin Yakovlev ; Anton Andreychuk ; Roni Stern
HIGHLIGHT:      In this paper we explore different ways to build a bounded-suboptimal SIPP and discuss their pros and cons.

80, TITLE:      An Effective Contextual Language Modeling Framework for Speech Summarization with Augmented Features
http://arxiv.org/abs/2006.01189
AUTHORS:        Shi-Yan Weng ; Tien-Hong Lo ; Berlin Chen
COMMENTS:       Accepted by EUSIPCO 2020
HIGHLIGHT:      In view of this, we in this paper contextualize and enhance the state-of-the-art BERT-based model for speech summarization, while its contributions are at least three-fold.

81, TITLE:      BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients
http://arxiv.org/abs/2006.01174
AUTHORS:        Maria de la Iglesia Vayá ; Jose Manuel Saborit ; Joaquim Angel Montell ; Antonio Pertusa ; Aurelia Bustos ; Miguel Cazorla ; Joaquin Galant ; Xavier Barber ; Domingo Orozco-Beltrán ; Francisco Garcia ; Marisa Caparrós ; Germán González ; Jose María Salinas
HIGHLIGHT:      In this work we describe BIMCV-COVID-19+ dataset, a large dataset from Medical Imaging Databank in Valencian Region Medical ImageBank (BIMCV) with chest X-ray images CXR (CR, DX) and computed tomography (CT) imaging of COVID-19+ patients along with their radiological findings and locations, pathologies, radiological reports (in Spanish), DICOM metadata, Polymerase chain reaction (PCR), Immunoglobulin G (IgG) and Immunoglobulin M (IgM) diagnostic antibody tests.

82, TITLE:      Lexical Normalization for Code-switched Data and its Effect on POS-tagging
http://arxiv.org/abs/2006.01175
AUTHORS:        Rob van der Goot ; Özlem Çetinoğlu
HIGHLIGHT:      In this paper, we focus on normalization in the context of code-switching.



==========Updates to Previous Papers==========
1, TITLE:       A fast and memory-efficient algorithm for smooth interpolation of polyrigid transformations: application to human joint tracking
http://arxiv.org/abs/2005.02159
AUTHORS:        K. Makki ; B. Borotikar ; M. Garetier ; S. Brochard ; D. Ben Salem ; F. Rousseau
HIGHLIGHT:      In this paper, we propose an algorithm using a matrix diagonalization based method for smooth interpolation of homogeneous polyrigid transformations of human joints during motion.

2, TITLE:       Info Intervention
http://arxiv.org/abs/1907.11090
AUTHORS:        Gong Heyang ; Zhu Ke
COMMENTS:       See more information on Causal AI: https://sites.google.com/view/minituring/home
HIGHLIGHT:      This paper introduces a new info intervention to tackle these two problems, and provides causal diagrams for communication and theoretical focus based on this info intervention.

3, TITLE:       Generalized Adaptation for Few-Shot Learning
http://arxiv.org/abs/1911.10807
AUTHORS:        Liang Song ; Jinlu Liu ; Yongqiang Qin
COMMENTS:       We found a bug in the script in reexamining some of this work. We decide to withdraw for further modification
HIGHLIGHT:      In this paper, we propose to use closed-form base learner, which constrains the adapting stage with pre-trained base model to get better generalized novel model.

4, TITLE:       An Algebraic Model For Quorum Systems
http://arxiv.org/abs/2005.08536
AUTHORS:        Alex Pellegrini ; Luca Zanolini
COMMENTS:       15 pages, 3 algorithms
HIGHLIGHT:      In this paper we give a new interpretation of quorum systems, starting with classical majority-based quorum systems and extending this to Byzantine quorum systems.

5, TITLE:       Self-adaptive Re-weighted Adversarial Domain Adaptation
http://arxiv.org/abs/2006.00223
AUTHORS:        Shanshan Wang ; Lei Zhang
COMMENTS:       to appear in IJCAI2020
HIGHLIGHT:      To address this problem, we present a self-adaptive re-weighted adversarial domain adaptation approach, which tries to enhance domain alignment from the perspective of conditional distribution.

6, TITLE:       PEGASUS: Pre-training with Extracted Gap-sentences for Abstractive Summarization
http://arxiv.org/abs/1912.08777
AUTHORS:        Jingqing Zhang ; Yao Zhao ; Mohammad Saleh ; Peter J. Liu
COMMENTS:       Added Human Evaluation results; Code link added; Accepted for ICML 2020
HIGHLIGHT:      In this work, we propose pre-training large Transformer-based encoder-decoder models on massive text corpora with a new self-supervised objective.

7, TITLE:       Meshlet Priors for 3D Mesh Reconstruction
http://arxiv.org/abs/2001.01744
AUTHORS:        Abhishek Badki ; Orazio Gallo ; Jan Kautz ; Pradeep Sen
COMMENTS:       To be presented at CVPR 2020
HIGHLIGHT:      We introduce meshlets, small patches of mesh that we use to learn local shape priors.

8, TITLE:       Mining Public Opinion on Twitter about Natural Disaster Response Using Machine Learning Techniques
http://arxiv.org/abs/2005.07019
AUTHORS:        Lingyu Meng ; Zhijie Sasha Dong ; Lauren Christenson ; Lawrence Fulton
COMMENTS:       27 pages, 12 figures
HIGHLIGHT:      Results in this paper demonstrate the feasibility and validation of the proposed research approach and provide relief agencies with insights into better disaster response.

9, TITLE:       Segmenting Unseen Industrial Components in a Heavy Clutter Using RGB-D Fusion and Synthetic Data
http://arxiv.org/abs/2002.03501
AUTHORS:        Seunghyeok Back ; Jongwon Kim ; Raeyoung Kang ; Seungjun Choi ; Kyoobin Lee
COMMENTS:       5 pages,6 figures, Accepted to ICIP 2020
HIGHLIGHT:      To tackle this problem, we present a synthetic data generation pipeline that randomizes textures via domain randomization to focus on the shape information.

10, TITLE:      Minimally Supervised Categorization of Text with Metadata
http://arxiv.org/abs/2005.00624
AUTHORS:        Yu Zhang ; Yu Meng ; Jiaxin Huang ; Frank F. Xu ; Xuan Wang ; Jiawei Han
COMMENTS:       10 pages; Accepted to SIGIR 2020
HIGHLIGHT:      In recognition of these two challenges, we propose \textsc{MetaCat}, a minimally supervised framework to categorize text with metadata.

11, TITLE:      Planning under non-rational perception of uncertain spatial costs
http://arxiv.org/abs/1904.02851
AUTHORS:        Aamodh Suresh ; Sonia Martinez
COMMENTS:       12 pages and 9 figures. This revision adds more results and comparisons along with more explanation
HIGHLIGHT:      This work investigates the design of risk-perception-aware motion-planning strategies that can incorporate non-rational perception of risks associated with uncertain spatial costs.

12, TITLE:      Pyramid Attention Networks for Image Restoration
http://arxiv.org/abs/2004.13824
AUTHORS:        Yiqun Mei ; Yuchen Fan ; Yulun Zhang ; Jiahui Yu ; Yuqian Zhou ; Ding Liu ; Yun Fu ; Thomas S. Huang ; Honghui Shi
HIGHLIGHT:      To solve this problem, we present a novel Pyramid Attention module for image restoration, which captures long-range feature correspondences from a multi-scale feature pyramid.

13, TITLE:      Graph Refinement based Airway Extraction using Mean-Field Networks and Graph Neural Networks
http://arxiv.org/abs/1811.08674
AUTHORS:        Raghavendra Selvan ; Thomas Kipf ; Max Welling ; Antonio Garcia-Uceda Juarez ; Jesper H Pedersen ; Jens Petersen ; Marleen de Bruijne
COMMENTS:       Accepted for publication at Medical Image Analysis. 14 pages
HIGHLIGHT:      We present two methods to perform graph refinement.

14, TITLE:      Neural Collaborative Reasoning
http://arxiv.org/abs/2005.08129
AUTHORS:        Hanxiong Chen ; Shaoyun Shi ; Yunqi Li ; Yongfeng Zhang
COMMENTS:       10 pages, 5 figures
HIGHLIGHT:      Inspired by recent progress on neural-symbolic machine learning, we propose a framework to integrate the power of embedding learning and logical reasoning, where the embeddings capture similarity patterns in data from perceptual perspectives, and the logic facilitates cognitive reasoning for informed decision making.

15, TITLE:      Coronavirus: Comparing COVID-19, SARS and MERS in the eyes of AI
http://arxiv.org/abs/2005.11524
AUTHORS:        Anas Tahir ; Yazan Qiblawey ; Amith Khandakar ; Tawsifur Rahman ; Uzair Khurshid ; Farayi Musharavati ; Serkan Kiranyaz ; Muhammad E. H. Chowdhury
COMMENTS:       10 Figures, 4 Tables
HIGHLIGHT:      In this work, authors used deep machine learning algorithms along with innovative image pre-processing techniques to distinguish COVID-19 images from SARS and MERS images.

16, TITLE:      A simple combinatorial proof for small model property of two-variable logic
http://arxiv.org/abs/1908.07239
AUTHORS:        Yanger Ma ; Tony Tan
HIGHLIGHT:      We present another proof for the well-known {\em small model property} of two-variable logic.

17, TITLE:      What am I Searching for: Zero-shot Target Identity Inference in Visual Search
http://arxiv.org/abs/1807.11926
AUTHORS:        Mengmi Zhang ; Gabriel Kreiman
COMMENTS:       Accepted for presentation at EPIC@CVPR2020 workshop
HIGHLIGHT:      As an example problem, here we consider how to decipher what a person is searching for by decoding their eye movement behavior.

18, TITLE:      Bi-directional Exponential Angular Triplet Loss for RGB-Infrared Person Re-Identification
http://arxiv.org/abs/2006.00878
AUTHORS:        Hanrong Ye ; Hong Liu ; Fanyang Meng ; Xia Li
COMMENTS:       First Submission: April 2019
HIGHLIGHT:      As an angularly discriminative feature space is important for classifying the human images based on their embedding vectors, in this paper, we propose a novel ranking loss function, named Bi-directional Exponential Angular Triplet Loss, to help learn an angularly separable common feature space by explicitly constraining the included angles between embedding vectors.

19, TITLE:      RxNN: A Framework for Evaluating Deep Neural Networks on Resistive Crossbars
http://arxiv.org/abs/1809.00072
AUTHORS:        Shubham Jain ; Abhronil Sengupta ; Kaushik Roy ; Anand Raghunathan
COMMENTS:       13 pages, 16 figures, Accepted in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD) 2020
HIGHLIGHT:      We present RxNN, a fast and accurate simulation framework to evaluate large-scale DNNs on resistive crossbar systems.

20, TITLE:      On Modeling ASR Word Confidence
http://arxiv.org/abs/1907.09636
AUTHORS:        Woojay Jeon ; Maxwell Jordan ; Mahesh Krishnamoorthy
COMMENTS:       Presented at IEEE ICASSP 2020, May 2020
HIGHLIGHT:      We present a new method for computing ASR word confidences that effectively mitigates the effect of ASR errors for diverse downstream applications, improves the word error rate of the 1-best result, and allows better comparison of scores across different models.

21, TITLE:      iASiS Open Data Graph: Automated Semantic Integration of Disease-Specific Knowledge
http://arxiv.org/abs/1912.08633
AUTHORS:        Anastasios Nentidis ; Konstantinos Bougiatiotis ; Anastasia Krithara ; Georgios Paliouras
COMMENTS:       6 pages, 2 figures, accepted in IEEE 33rd International Symposium on Computer Based Medical Systems (CBMS2020)
HIGHLIGHT:      In this study, we propose a framework to automatically retrieve and integrate disease-specific knowledge into an up-to-date semantic graph, the iASiS Open Data Graph.

22, TITLE:      Improving the Robustness of Graphs through Reinforcement Learning and Graph Neural Networks
http://arxiv.org/abs/2001.11279
AUTHORS:        Victor-Alexandru Darvariu ; Stephen Hailes ; Mirco Musolesi
COMMENTS:       12 pages, 3 figures
HIGHLIGHT:      In this work, we present RNet-DQN, a solution that uses Reinforcement Learning to address the problem of improving the robustness of graphs in the presence of random and targeted removals of nodes.

23, TITLE:      Planning Beyond the Sensing Horizon Using a Learned Context
http://arxiv.org/abs/1908.09171
AUTHORS:        Michael Everett ; Justin Miller ; Jonathan P. How
HIGHLIGHT:      Therefore, this work proposes a novel formulation of utilizing context for planning as an image-to-image translation problem, which is shown to extract terrain context from semantic gridmaps, into a metric that an exploration-based planner can use.

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

25, TITLE:      Speak to your Parser: Interactive Text-to-SQL with Natural Language Feedback
http://arxiv.org/abs/2005.02539
AUTHORS:        Ahmed Elgohary ; Saghar Hosseini ; Ahmed Hassan Awadallah
COMMENTS:       ACL 2020
HIGHLIGHT:      In this paper, we investigate a more interactive scenario where humans can further interact with the system by providing free-form natural language feedback to correct the system when it generates an inaccurate interpretation of an initial utterance.

26, TITLE:      ADDMC: Weighted Model Counting with Algebraic Decision Diagrams
http://arxiv.org/abs/1907.05000
AUTHORS:        Jeffrey M. Dudek ; Vu H. N. Phan ; Moshe Y. Vardi
COMMENTS:       Presented at AAAI 2020
HIGHLIGHT:      We present an algorithm to compute exact literal-weighted model counts of Boolean formulas in Conjunctive Normal Form.

27, TITLE:      Beyond #CSP: A Dichotomy for Counting Weighted Eulerian Orientations with ARS
http://arxiv.org/abs/1904.02362
AUTHORS:        Jin-Yi Cai ; Zhiguo Fu ; Shuai Shao
COMMENTS:       37 pages, 2 figures
HIGHLIGHT:      We show that the class of weighted Eulerian orientation problems subsumes all weighted counting constraint satisfaction problems (#CSP) on Boolean variables.

28, TITLE:      Bi-direction Context Propagation Network for Real-time Semantic Segmentation
http://arxiv.org/abs/2005.11034
AUTHORS:        Shijie Hao ; Yuan Zhou ; Yanrong Guo ; Richang Hong
COMMENTS:       9 pages, 6 figures
HIGHLIGHT:      To address this problem, we propose the Bi-direction Context Propagation Network (BCPNet) by leveraging both spatial and context information.

29, TITLE:      Adaptive Explainable Neural Networks (AxNNs)
http://arxiv.org/abs/2004.02353
AUTHORS:        Jie Chen ; Joel Vaughan ; Vijayan N. Nair ; Agus Sudjianto
HIGHLIGHT:      We develop a new framework called Adaptive Explainable Neural Networks (AxNN) for achieving the dual goals of good predictive performance and model interpretability.

30, TITLE:      Joint Demosaicing and Super-Resolution (JDSR): Network Design and Perceptual Optimization
http://arxiv.org/abs/1911.03558
AUTHORS:        Xuan Xu ; Yanfang Ye ; Xin Li
COMMENTS:       To appear in IEEE Transactions on Computational Imaging
HIGHLIGHT:      In this paper, we propose an end-to-end optimization solution to the JDSR problem and demonstrate its practical significance in computational imaging.

31, TITLE:      Moving Down the Long Tail of Word Sense Disambiguation with Gloss-Informed Biencoders
http://arxiv.org/abs/2005.02590
AUTHORS:        Terra Blevins ; Luke Zettlemoyer
COMMENTS:       Accepted to ACL 2020; current version corrects typos and formatting
HIGHLIGHT:      We propose a bi-encoder model that independently embeds (1) the target word with its surrounding context and (2) the dictionary definition, or gloss, of each sense.

32, TITLE:      Self-Adjusting Evolutionary Algorithms for Multimodal Optimization
http://arxiv.org/abs/2004.03266
AUTHORS:        Amirhossein Rajabi ; Carsten Witt
COMMENTS:       26 pages. Full version of a paper appearing at GECCO 2020
HIGHLIGHT:      Recent theoretical research has shown that self-adjusting and self-adaptive mechanisms can provably outperform static settings in evolutionary algorithms for binary search spaces.

33, TITLE:      CRVOS: Clue Refining Network for Video Object Segmentation
http://arxiv.org/abs/2002.03651
AUTHORS:        Suhwan Cho ; MyeongAh Cho ; Tae-young Chung ; Heansung Lee ; Sangyoun Lee
COMMENTS:       ICIP 2020. Code: https://github.com/suhwan-cho/CRVOS
HIGHLIGHT:      In this work, we propose a simple specifier, referred to as the Clue, which consists of the previous frame's coarse mask and coordinates information.

34, TITLE:      Generating Diverse Story Continuations with Controllable Semantics
http://arxiv.org/abs/1909.13434
AUTHORS:        Lifu Tu ; Xiaoan Ding ; Dong Yu ; Kevin Gimpel
COMMENTS:       WNGT 2019
HIGHLIGHT:      We propose a simple and effective modeling framework for controlled generation of multiple, diverse outputs.

35, TITLE:      Semantic Object Accuracy for Generative Text-to-Image Synthesis
http://arxiv.org/abs/1910.13321
AUTHORS:        Tobias Hinz ; Stefan Heinrich ; Stefan Wermter
COMMENTS:       Added a user study to verify results. Code available at https://github.com/tohinz/semantic-object-accuracy-for-generative-text-to-image-synthesis
HIGHLIGHT:      To address these challenges we introduce a new model that explicitly models individual objects within an image and a new evaluation metric called Semantic Object Accuracy (SOA) that specifically evaluates images given an image caption.

36, TITLE:      Køpsala: Transition-Based Graph Parsing via Efficient Training and Effective Encoding
http://arxiv.org/abs/2005.12094
AUTHORS:        Daniel Hershcovich ; Miryam de Lhoneux ; Artur Kulmizev ; Elham Pejhan ; Joakim Nivre
COMMENTS:       IWPT shared task 2020
HIGHLIGHT:      We present K{\o}psala, the Copenhagen-Uppsala system for the Enhanced Universal Dependencies Shared Task at IWPT 2020.

37, TITLE:      Neural Architecture Search with Reinforce and Masked Attention Autoregressive Density Estimators
http://arxiv.org/abs/2006.00939
AUTHORS:        Chepuri Shri Krishna ; Ashish Gupta ; Himanshu Rai ; Swarnim Narayan
HIGHLIGHT:      In this paper, we present a reinforcement learning algorithm based on policy gradient that uses an attention-based autoregressive model to design the policy network.

38, TITLE:      Adaptive Stress Testing: Finding Likely Failure Events with Reinforcement Learning
http://arxiv.org/abs/1811.02188
AUTHORS:        Ritchie Lee ; Ole J. Mengshoel ; Anshu Saksena ; Ryan Gardner ; Daniel Genin ; Joshua Silbermann ; Michael Owen ; Mykel J. Kochenderfer
COMMENTS:       36 pages, 17 figures
HIGHLIGHT:      This article presents adaptive stress testing (AST), a framework for finding the most likely path to a failure event in simulation.

39, TITLE:      Do We Train on Test Data? Purging CIFAR of Near-Duplicates
http://arxiv.org/abs/1902.00423
AUTHORS:        Björn Barz ; Joachim Denzler
COMMENTS:       Journal of Imaging
HIGHLIGHT:      The CIFAR-10 and CIFAR-100 datasets are two of the most heavily benchmarked datasets in computer vision and are often used to evaluate novel methods and model architectures in the field of deep learning. To eliminate this bias, we provide the "fair CIFAR" (ciFAIR) dataset, where we replaced all duplicates in the test sets with new images sampled from the same domain.

40, TITLE:      Selection Heuristics on Semantic Genetic Programming for Classification Problems
http://arxiv.org/abs/1907.07066
AUTHORS:        Claudia N. Sánchez ; Mario Graff
HIGHLIGHT:      We analyze the performance of these heuristics against random selection, state-of-the-art selection schemes, and 18 classifiers, including auto-machine-learning techniques, on 30 classification problems with a variable number of samples, variables, and classes.

41, TITLE:      Transferring Inductive Biases through Knowledge Distillation
http://arxiv.org/abs/2006.00555
AUTHORS:        Samira Abnar ; Mostafa Dehghani ; Willem Zuidema
HIGHLIGHT:      In this paper, we explore the power of knowledge distillation for transferring the effect of inductive biases from one model to another.

42, TITLE:      Variational Bayesian Inference for Crowdsourcing Predictions
http://arxiv.org/abs/2006.00778
AUTHORS:        Desmond Cai ; Duc Thien Nguyen ; Shiau Hong Lim ; Laura Wynter
COMMENTS:       7 pages
HIGHLIGHT:      To do so, we propose a Bayesian approach aimed specifically at alleviating overfitting, a typical impediment to accurate prediction models in practice.

43, TITLE:      MVStylizer: An Efficient Edge-Assisted Video Photorealistic Style Transfer System for Mobile Phones
http://arxiv.org/abs/2005.11630
AUTHORS:        Ang Li ; Chunpeng Wu ; Yiran Chen ; Bin Ni
HIGHLIGHT:      To address this challenge, we propose MVStylizer, an efficient edge-assisted photorealistic video style transfer system for mobile phones.

44, TITLE:      Cross-Domain Few-Shot Learning with Meta Fine-Tuning
http://arxiv.org/abs/2005.10544
AUTHORS:        John Cai ; Sheng Mei Shen
COMMENTS:       4 Tables, 2 Figures
HIGHLIGHT:      In this paper, we tackle the new Cross-Domain Few-Shot Learning benchmark proposed by the CVPR 2020 Challenge.

45, TITLE:      Self-supervised Keypoint Correspondences for Multi-Person Pose Estimation and Tracking in Videos
http://arxiv.org/abs/2004.12652
AUTHORS:        Umer Rafi ; Andreas Doering ; Bastian Leibe ; Juergen Gall
COMMENTS:       Submitted to ECCV 2020
HIGHLIGHT:      To address this issue, we propose an approach that relies on keypoint correspondences for associating persons in videos.

46, TITLE:      Generating Diverse and Consistent QA pairs from Contexts with Information-Maximizing Hierarchical Conditional VAEs
http://arxiv.org/abs/2005.13837
AUTHORS:        Dong Bok Lee ; Seanie Lee ; Woo Tae Jeong ; Donghwan Kim ; Sung Ju Hwang
COMMENTS:       ACL 2020
HIGHLIGHT:      In this work, we propose a hierarchical conditional variational autoencoder (HCVAE) for generating QA pairs given unstructured texts as contexts, while maximizing the mutual information between generated QA pairs to ensure their consistency.

47, TITLE:      Towards a Human-Centred Cognitive Model of Visuospatial Complexity in Everyday Driving
http://arxiv.org/abs/2006.00059
AUTHORS:        Vasiliki Kondyli ; Mehul Bhatt ; Jakob Suchan
COMMENTS:       9th European Starting AI Researchers Symposium (STAIRS), at ECAI 2020, the 24th European Conference on Artificial Intelligence (ECAI)., Santiago de Compostela, Spain
HIGHLIGHT:      We develop a human-centred, cognitive model of visuospatial complexity in everyday, naturalistic driving conditions.

48, TITLE:      Persistence Curves: A canonical framework for summarizing persistence diagrams
http://arxiv.org/abs/1904.07768
AUTHORS:        Yu-Min Chung ; Austin Lawson
HIGHLIGHT:      In this paper, our main contribution consists of three components.

49, TITLE:      Evolution of Cooperative Hunting in Artificial Multi-layered Societies
http://arxiv.org/abs/2005.11580
AUTHORS:        Honglin Bao ; Wolfgang Banzhaf
COMMENTS:       Conflict of interest with my pervious collaborators, so I ask for pre-print retracting. Thanks
HIGHLIGHT:      In this paper, an agent-based model is proposed to study the evolution of cooperative hunting behaviors in an artificial society.

50, TITLE:      TripPy: A Triple Copy Strategy for Value Independent Neural Dialog State Tracking
http://arxiv.org/abs/2005.02877
AUTHORS:        Michael Heck ; Carel van Niekerk ; Nurul Lubis ; Christian Geishauser ; Hsien-Chin Lin ; Marco Moresi ; Milica Gašić
COMMENTS:       10 pages, 6 figures, to be published in Proceedings of the 21st Annual SIGdial Meeting on Discourse and Dialogue
HIGHLIGHT:      In this paper we present a new approach to DST which makes use of various copy mechanisms to fill slots with values.

51, TITLE:      Is Depth Really Necessary for Salient Object Detection?
http://arxiv.org/abs/2006.00269
AUTHORS:        Jiawei Zhao ; Yifan Zhao ; Jia Li ; Xiaowu Chen
HIGHLIGHT:      Taking the advantages of RGB and RGBD methods, we propose a novel depth-aware salient object detection framework, which has following superior designs: 1) It only takes the depth information as training data while only relies on RGB information in the testing phase.

52, TITLE:      Group-Connected Multilayer Perceptron Networks
http://arxiv.org/abs/1912.09600
AUTHORS:        Mohammad Kachuee ; Sajad Darabi ; Shayan Fazeli ; Majid Sarrafzadeh
HIGHLIGHT:      In this paper, we propose Group-Connected Multilayer Perceptron (GMLP) networks to enable deep representation learning in these domains.

53, TITLE:      Automatic acute ischemic stroke lesion segmentation using semi-supervised learning
http://arxiv.org/abs/1908.03735
AUTHORS:        Bin Zhao ; Shuxue Ding ; Hong Wu ; Guohua Liu ; Chen Cao ; Song Jin ; Zhiyang Liu
HIGHLIGHT:      In this paper, we propose a semi-supervised method to automatically segment AIS lesions in diffusion weighted images and apparent diffusion coefficient maps.

54, TITLE:      Speak2Label: Using Domain Knowledge for Creating a Large Scale Driver Gaze Zone Estimation Dataset
http://arxiv.org/abs/2004.05973
AUTHORS:        Shreya Ghosh ; Abhinav Dhall ; Garima Sharma ; Sarthak Gupta ; Nicu Sebe
HIGHLIGHT:      In this paper, a fully automatic technique for labelling an image based gaze behavior dataset for driver gaze zone estimation is proposed.

55, TITLE:      3D Solid Spherical Bispectrum CNNs for Biomedical Texture Analysis
http://arxiv.org/abs/2004.13371
AUTHORS:        Valentin Oreiller ; Vincent Andrearczyk ; Julien Fageot ; John O. Prior ; Adrien Depeursinge
COMMENTS:       10 pages
HIGHLIGHT:      In this work, we obtain LRI operators via the local projection of the image on the spherical harmonics basis, followed by the computation of the bispectrum, which shares and extends the invariance properties of the spectrum.

56, TITLE:      Analyzing COVID-19 on Online Social Media: Trends, Sentiments and Emotions
http://arxiv.org/abs/2005.14464
AUTHORS:        Xiaoya Li ; Mingxin Zhou ; Jiawei Wu ; Arianna Yuan ; Fei Wu ; Jiwei Li
HIGHLIGHT:      In this paper, we perform a comprehensive analysis on the affective trajectories of the American people and the Chinese people based on Twitter and Weibo posts between January 20th, 2020 and May 11th 2020.

57, TITLE:      A Faster FPTAS for Knapsack Problem With Cardinality Constraint
http://arxiv.org/abs/1902.00919
AUTHORS:        Wenxin Li ; Joohyun Lee ; Ness Shroff
HIGHLIGHT:      In this paper we close the space-time tradeoff exhibited in the state-of-the-art by designing a new FPTAS with a run-time of $\widetilde{O}(n+z^{2}/\varepsilon^{2})$, while simultaneously reaching the $O(n+z^{2}/\varepsilon)$ space bound.

58, TITLE:      SSM-Net for Plants Disease Identification in Low Data Regime
http://arxiv.org/abs/2005.13140
AUTHORS:        Shruti Jadon
COMMENTS:       5 pages, 6 Figures
HIGHLIGHT:      In this paper, we propose a new metrics-based few-shot learning SSM net architecture, which consists of stacked siamese and matching network components to solve the problem of disease detection in low data regimes.

59, TITLE:      Long Short-Term Memory with Gate and State Level Fusion for Light Field-Based Face Recognition
http://arxiv.org/abs/1905.04421
AUTHORS:        Alireza Sepas-Moghaddam ; Ali Etemad ; Fernando Pereira ; Paulo Lobato Correia
COMMENTS:       Submitted to IEEE TIFS
HIGHLIGHT:      In this context, this paper proposes two novel LSTM cell architectures that are able to jointly learn from multiple sequences simultaneously acquired, targeting to create richer and more effective models for recognition tasks.

60, TITLE:      Improved Search Strategies for Determining Facial Expression
http://arxiv.org/abs/1812.02897
AUTHORS:        Michael Bao ; David Hyde ; Xinru Hua ; Ronald Fedkiw
HIGHLIGHT:      Motivated by a geometric interpretation of the linearized search space, we propose an approach that ameliorates overfitting without the need for regularization terms that restrict the expressiveness of the underlying model.