2020.09.02.txt

==========New Papers==========
1, TITLE:       Solving the single-track train scheduling problem via Deep Reinforcement Learning
http://arxiv.org/abs/2009.00433
AUTHORS:        Valerio Agasucci ; Giorgio Grani ; Leonardo Lamorgese
COMMENTS:       12 pages, 4 figures (2 b&w)
CATEGORY:       cs.AI [cs.AI, 90B36, I.2.8]
HIGHLIGHT:      In this paper, machine learning-based methods are investigated when dealing with the train dispatching problem.

2, TITLE:       More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings
http://arxiv.org/abs/2009.00318
AUTHORS:        Andreea Iana ; Heiko Paulheim
COMMENTS:       Accepted at the Workshop on Combining Symbolic and Sub-symbolic methods and their Applications (CSSA 2020)
CATEGORY:       cs.AI [cs.AI]
HIGHLIGHT:      In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties.

3, TITLE:       XCSP3-core: A Format for Representing Constraint Satisfaction/Optimization Problems
http://arxiv.org/abs/2009.00514
AUTHORS:        Frédéric Boussemart ; Christophe Lecoutre ; Gilles Audemard ; Cédric Piette
CATEGORY:       cs.AI [cs.AI]
HIGHLIGHT:      In this document, we introduce XCSP3-core, a subset of XCSP3 that allows us to represent constraint satisfaction/optimization problems.

4, TITLE:       Visual Causality Analysis of Event Sequence Data
http://arxiv.org/abs/2009.00219
AUTHORS:        Zhuochen Jin ; Shunan Guo ; Nan Chen ; Daniel Weiskopf ; David Gotz ; Nan Cao
CATEGORY:       cs.AI [cs.AI, cs.HC]
HIGHLIGHT:      In this paper, we introduce a visual analytics method for recovering causalities in event sequence data.

5, TITLE:       Machine Reasoning Explainability
http://arxiv.org/abs/2009.00418
AUTHORS:        Kristijonas Cyras ; Ramamurthy Badrinath ; Swarup Kumar Mohalik ; Anusha Mujumdar ; Alexandros Nikou ; Alessandro Previti ; Vaishnavi Sundararajan ; Aneta Vulgarakis Feljan
COMMENTS:       37 pages of content plus 20 pages of references
CATEGORY:       cs.AI [cs.AI, I.2.0; I.2.m; F.4.1]
HIGHLIGHT:      We hereby aim to provide a selective overview of MR explainability techniques and studies in hopes that insights from this long track of research will complement well the current XAI landscape.

6, TITLE:       "It's Unwieldy and It Takes a Lot of Time." Challenges and Opportunities for Creating Agents in Commercial Games
http://arxiv.org/abs/2009.00541
AUTHORS:        Mikhail Jacob ; Sam Devlin ; Katja Hofmann
COMMENTS:       7 pages, 3 figures, to be published in the 16th AAAI Conference on Artificial Intelligence and Interactive Digital Entertainment (AIIDE-20)
CATEGORY:       cs.AI [cs.AI]
HIGHLIGHT:      Our study revealed several open challenges ranging from design to implementation and evaluation.

7, TITLE:       Landscape of Machine Implemented Ethics
http://arxiv.org/abs/2009.00335
AUTHORS:        Vivek Nallur
COMMENTS:       25 pages
CATEGORY:       cs.AI [cs.AI]
HIGHLIGHT:      This paper surveys the state-of-the-art in machine ethics, that is, considerations of how to implement ethical behaviour in robots, unmanned autonomous vehicles, or software systems.

8, TITLE:       Cross-modal Knowledge Reasoning for Knowledge-based Visual Question Answering
http://arxiv.org/abs/2009.00145
AUTHORS:        Jing Yu ; Zihao Zhu ; Yujing Wang ; Weifeng Zhang ; Yue Hu ; Jianlong Tan
COMMENTS:       Accepted at Pattern Recognition
CATEGORY:       cs.AI [cs.AI, cs.CV]
HIGHLIGHT:      Inspired by the human cognition theory, in this paper, we depict an image by multiple knowledge graphs from the visual, semantic and factual views.

9, TITLE:       A Benchmark for Multi-UAV Task Assignment of an Extended Team Orienteering Problem
http://arxiv.org/abs/2009.00363
AUTHORS:        Kun Xiao ; Junqi Lu ; Ying Nie ; Lan Ma ; Xiangke Wang ; Guohui Wang
CATEGORY:       cs.AI [cs.AI, cs.SY, eess.SY]
HIGHLIGHT:      A benchmark for multi-UAV task assignment is presented in order to evaluate different algorithms.

10, TITLE:      PYCSP3: Modeling Combinatorial Constrained Problems in Python
http://arxiv.org/abs/2009.00326
AUTHORS:        Christophe Lecoutre ; Nicolas Szczepanski
CATEGORY:       cs.AI [cs.AI]
HIGHLIGHT:      In this document, we introduce PYCSP$3$, a Python library that allows us to write models of combinatorial constrained problems in a simple and declarative way.

11, TITLE:      PNEL: Pointer Network based End-To-End Entity Linking over Knowledge Graphs
http://arxiv.org/abs/2009.00106
AUTHORS:        Debayan Banerjee ; Debanjan Chaudhuri ; Mohnish Dubey ; Jens Lehmann
CATEGORY:       cs.CL [cs.CL, cs.AI]
HIGHLIGHT:      In this work we present a novel approach to end-to-end EL by applying the popular Pointer Network model, which achieves competitive performance.

12, TITLE:      SuperPAL: Supervised Proposition ALignment for Multi-Document Summarization and Derivative Sub-Tasks
http://arxiv.org/abs/2009.00590
AUTHORS:        Ori Ernst ; Ori Shapira ; Ramakanth Pasunuru ; Michael Lepioshkin ; Jacob Goldberger ; Mohit Bansal ; Ido Dagan
CATEGORY:       cs.CL [cs.CL]
HIGHLIGHT:      As a first step, we present an annotation methodology by which we create gold standard development and test sets for summary-source alignment, and suggest its utility for tuning and evaluating effective alignment algorithms, as well as for properly evaluating MDS subtasks. Second, we introduce a new large-scale alignment dataset for training, with which an automatic alignment model was trained. Finally, we release three additional datasets (for salience, clustering and generation), naturally derived from our alignment datasets.

13, TITLE:      Extracting Semantic Concepts and Relations from Scientific Publications by Using Deep Learning
http://arxiv.org/abs/2009.00331
AUTHORS:        Fatima N. AL-Aswadi ; Huah Yong Chan ; Keng Hoon Gan
COMMENTS:       Proposal
CATEGORY:       cs.CL [cs.CL]
HIGHLIGHT:      The aim of this paper is to introduce a proposal of automatically extracting semantic concepts and relations from scientific publications.

14, TITLE:      A Framework For Contrastive Self-Supervised Learning And Designing A New Approach
http://arxiv.org/abs/2009.00104
AUTHORS:        William Falcon ; Kyunghyun Cho
CATEGORY:       cs.CV [cs.CV, cs.LG]
HIGHLIGHT:      We present a conceptual framework that characterizes CSL approaches in five aspects (1) data augmentation pipeline, (2) encoder selection, (3) representation extraction, (4) similarity measure, and (5) loss function.

15, TITLE:      Personalization in Human Activity Recognition
http://arxiv.org/abs/2009.00268
AUTHORS:        Anna Ferrari ; Daniela Micucci ; Marco Mobilio ; Paolo Napoletano
COMMENTS:       2 pages
CATEGORY:       cs.CV [cs.CV, cs.LG]
HIGHLIGHT:      In this paper we explore the possibility of exploiting physical characteristics and signal similarity to achieve better results with respect to deep learning classifiers that do not rely on this information.

16, TITLE:      RangeRCNN: Towards Fast and Accurate 3D Object Detection with Range Image Representation
http://arxiv.org/abs/2009.00206
AUTHORS:        Zhidong Liang ; Ming Zhang ; Zehan Zhang ; Xian Zhao ; Shiliang Pu
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      We present RangeRCNN, a novel and effective 3D object detection framework based on the range image representation.

17, TITLE:      Distinctive 3D local deep descriptors
http://arxiv.org/abs/2009.00258
AUTHORS:        Fabio Poiesi ; Davide Boscaini
COMMENTS:       Accepted in IEEE International Conference on Pattern Recognition 2020
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      We present a simple but yet effective method for learning distinctive 3D local deep descriptors (DIPs) that can be used to register point clouds without requiring an initial alignment.

18, TITLE:      Multimodal Aggregation Approach for Memory Vision-Voice Indoor Navigation with Meta-Learning
http://arxiv.org/abs/2009.00402
AUTHORS:        Liqi Yan ; Dongfang Liu ; Yaoxian Song ; Changbin Yu
COMMENTS:       8 pages, 6 figures, 2 tables, accepted at 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS 2020)
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we present a novel indoor navigation model called Memory Vision-Voice Indoor Navigation (MVV-IN), which receives voice commands and analyzes multimodal information of visual observation in order to enhance robots' environment understanding.

19, TITLE:      GIF: Generative Interpretable Faces
http://arxiv.org/abs/2009.00149
AUTHORS:        Partha Ghosh ; Pravir Singh Gupta ; Roy Uziel ; Anurag Ranjan ; Michael Black ; Timo Bolkart
CATEGORY:       cs.CV [cs.CV, cs.AI, cs.GR, cs.LG, stat.AP]
HIGHLIGHT:      On one end of the spectrum, 3D face modeling methods provide parametric control but tend to generate unrealistic images, while on the other end, generative 2D models like GANs (Generative Adversarial Networks) output photo-realistic face images, but lack explicit control.

20, TITLE:      Inducing Predictive Uncertainty Estimation for Face Recognition
http://arxiv.org/abs/2009.00603
AUTHORS:        Weidi Xie ; Jeffrey Byrne ; Andrew Zisserman
COMMENTS:       To Appear at the British Machine Vision Conference (BMVC), 2020
CATEGORY:       cs.CV [cs.CV, cs.LG]
HIGHLIGHT:      To this end, we propose a method for generating image quality training data automatically from 'mated-pairs' of face images, and use the generated data to train a lightweight Predictive Confidence Network, termed as PCNet, for estimating the confidence score of a face image.

21, TITLE:      LodoNet: A Deep Neural Network with 2D Keypoint Matchingfor 3D LiDAR Odometry Estimation
http://arxiv.org/abs/2009.00164
AUTHORS:        Ce Zheng ; Yecheng Lyu ; Ming Li ; Ziming Zhang
COMMENTS:       In 28th ACM International Conference on Multimedia, 9 pages
CATEGORY:       cs.CV [cs.CV, I.5.4]
HIGHLIGHT:      In contrast, motivated by the success of image based feature extractors, we propose to transfer the LiDAR frames to image space and reformulate the problem as image feature extraction.

22, TITLE:      Automatic Radish Wilt Detection Using Image Processing Based Techniques and Machine Learning Algorithm
http://arxiv.org/abs/2009.00173
AUTHORS:        Asif Ashraf Patankar ; Hyeonjoon Moon
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose a segmentation and extraction-based technique to detect fusarium wilt in radish crops.

23, TITLE:      A High-Level Description and Performance Evaluation of Pupil Invisible
http://arxiv.org/abs/2009.00508
AUTHORS:        Marc Tonsen ; Chris Kay Baumann ; Kai Dierkes
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      Here, we present an extensive evaluation of its gaze-estimation capabilities.

24, TITLE:      A Review of Single-Source Deep Unsupervised Visual Domain Adaptation
http://arxiv.org/abs/2009.00155
AUTHORS:        Sicheng Zhao ; Xiangyu Yue ; Shanghang Zhang ; Bo Li ; Han Zhao ; Bichen Wu ; Ravi Krishna ; Joseph E. Gonzalez ; Alberto L. Sangiovanni-Vincentelli ; Sanjit A. Seshia ; Kurt Keutzer
CATEGORY:       cs.CV [cs.CV, cs.LG, eess.IV]
HIGHLIGHT:      In this paper, we review the latest single-source deep unsupervised domain adaptation methods focused on visual tasks and discuss new perspectives for future research.

25, TITLE:      Heatmap Regression via Randomized Rounding
http://arxiv.org/abs/2009.00225
AUTHORS:        Baosheng Yu ; Dacheng Tao
COMMENTS:       Under review
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we formally analyze the quantization error of vanilla heatmap regression and propose a simple yet effective quantization system to address the sub-pixel localization problem.

26, TITLE:      Semantics-aware Adaptive Knowledge Distillation for Sensor-to-Vision Action Recognition
http://arxiv.org/abs/2009.00210
AUTHORS:        Yang Liu ; Guanbin Li ; Liang Lin
COMMENTS:       Under review by IEEE Transactions on Image Processing. This paper focuses on the sensor-to-vision heterogenous action recognition problem. Codes is available in https://github.com/YangLiu9208/SAKDN
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose a novel framework, named Semantics-aware Adaptive Knowledge Distillation Networks (SAKDN), to enhance action recognition in vision-sensor modality (videos) by adaptively transferring and distilling the knowledge from multiple wearable sensors.

27, TITLE:      Temporal Continuity Based Unsupervised Learning for Person Re-Identification
http://arxiv.org/abs/2009.00242
AUTHORS:        Usman Ali ; Bayram Bayramli ; Hongtao Lu
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      To overcome this problem, we propose an unsupervised center-based clustering approach capable of progressively learning and exploiting the underlying re-id discriminative information from temporal continuity within a camera.

28, TITLE:      Multi-channel Transformers for Multi-articulatory Sign Language Translation
http://arxiv.org/abs/2009.00299
AUTHORS:        Necati Cihan Camgoz ; Oscar Koller ; Simon Hadfield ; Richard Bowden
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper we tackle the multi-articulatory sign language translation task and propose a novel multi-channel transformer architecture.

29, TITLE:      LaDDer: Latent Data Distribution Modelling with a Generative Prior
http://arxiv.org/abs/2009.00088
AUTHORS:        Shuyu Lin ; Ronald Clark
COMMENTS:       Accepted to BMVC 2020. Code and demos are available at: https://github.com/lin-shuyu/ladder-latent-data-distribution-modelling
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we show that the performance of a learnt generative model is closely related to the model's ability to accurately represent the inferred \textbf{latent data distribution}, i.e. its topology and structural properties.

30, TITLE:      Object Detection-Based Variable Quantization Processing
http://arxiv.org/abs/2009.00189
AUTHORS:        Likun Liu ; Hua Qi
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose a preprocessing method for conventional image and video encoders that can make these existing encoders content-aware.

31, TITLE:      Utilizing Satellite Imagery Datasets and Machine Learning Data Models to Evaluate Infrastructure Change in Undeveloped Regions
http://arxiv.org/abs/2009.00185
AUTHORS:        Kyle McCullough ; Andrew Feng ; Meida Chen ; Ryan McAlinden
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      A goal of this research is to allow automated monitoring for largescale infrastructure projects, such as railways, to determine reliable metrics that define and predict the direction construction initiatives could take, allowing for a directed monitoring via narrowed and targeted satellite imagery requests.

32, TITLE:      Deep Ice Layer Tracking and Thickness Estimation using Fully Convolutional Networks
http://arxiv.org/abs/2009.00191
AUTHORS:        Maryam Rahnemoonfar ; Debvrat Varshney ; Masoud Yari ; John Paden
CATEGORY:       cs.CV [cs.CV, eess.IV]
HIGHLIGHT:      In this paper, we introduce a novel way of estimating the thickness of each internal ice layer using Snow Radar images and Fully Convolutional Networks.

33, TITLE:      Online Multi-Object Tracking and Segmentation with GMPHD Filter and Simple Affinity Fusion
http://arxiv.org/abs/2009.00100
AUTHORS:        Young-min Song ; Moongu Jeon
COMMENTS:       5th BMTT MOTChallenge Workshop: Multi-Object Tracking and Segmentation In Conjunction with the Conference on Computer Vision and Pattern Recognition, CVPR 2020. (8th Place Track 1, 4th Place Track 2, and 5th Place Track 3)
CATEGORY:       cs.CV [cs.CV, cs.LG, cs.MM]
HIGHLIGHT:      In this paper, we propose a highly practical fully online multi-object tracking and segmentation (MOTS) method that uses instance segmentation results as an input in video.

34, TITLE:      To augment or not to augment? Data augmentation in user identification based on motion sensors
http://arxiv.org/abs/2009.00300
AUTHORS:        Cezara Benegui ; Radu Tudor Ionescu
COMMENTS:       Extended version (12 pages, 2 figures) of our paper (9 pages) accepted at ICONIP 2020
CATEGORY:       cs.CV [cs.CV, cs.LG, eess.SP]
HIGHLIGHT:      In this paper, we study several data augmentation techniques in the quest of finding useful augmentation methods for motion sensor data. We propose a set of four research questions related to data augmentation in the context of few-shot user identification based on motion sensor signals.

35, TITLE:      LiftFormer: 3D Human Pose Estimation using attention models
http://arxiv.org/abs/2009.00348
AUTHORS:        Adrian Llopart
COMMENTS:       9 pages, 2 figures
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      Thus, we propose the usage of these models to obtain more accurate 3D predictions by leveraging temporal information using attention mechanisms on ordered sequences human poses in videos.

36, TITLE:      Generalized Zero-Shot Learning via VAE-Conditioned Generative Flow
http://arxiv.org/abs/2009.00303
AUTHORS:        Yu-Chao Gu ; Le Zhang ; Yun Liu ; Shao-Ping Lu ; Ming-Ming Cheng
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      More specifically, we propose a conditional version of generative flows for GZSL, i.e., VAE-Conditioned Generative Flow (VAE-cFlow).

37, TITLE:      MORPH-DSLAM: Model Order Reduction for PHysics-based Deformable SLAM
http://arxiv.org/abs/2009.00576
AUTHORS:        Alberto Badias ; Iciar Alfaro ; David Gonzalez ; Francisco Chinesta ; Elias Cueto
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      We propose a new methodology to estimate the 3D displacement field of deformable objects from video sequences using standard monocular cameras.

38, TITLE:      A Short Review on Data Modelling for Vector Fields
http://arxiv.org/abs/2009.00577
AUTHORS:        Jun Li ; Wanrong Hong ; Yusheng Xiang
COMMENTS:       18 pages, 0 figures
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      Machine learning methods based on statistical principles have proven highly successful in dealing with a wide variety of data analysis and analytics tasks.

39, TITLE:      3D-DEEP: 3-Dimensional Deep-learning based on elevation patterns forroad scene interpretation
http://arxiv.org/abs/2009.00330
AUTHORS:        A. Hernández ; S. Woo ; H. Corrales ; I. Parra ; E. Kim ; D. F. Llorca ; M. A. Sotelo
COMMENTS:       "This work has been accepted for publication at IEEE Intelligent Vehicle Symposium 2020"
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      The developed models were trained and validated over Cityscapes dataset using just fine annotation examples with 19 different training classes, and over KITTI road dataset.

40, TITLE:      A Primer on Motion Capture with Deep Learning: Principles, Pitfalls and Perspectives
http://arxiv.org/abs/2009.00564
AUTHORS:        Alexander Mathis ; Steffen Schneider ; Jessy Lauer ; Mackenzie W. Mathis
COMMENTS:       Review, 21 pages, 8 figures and 5 boxes
CATEGORY:       cs.CV [cs.CV, cs.LG, q-bio.NC, q-bio.QM]
HIGHLIGHT:      In this primer we review the budding field of motion capture with deep learning.

41, TITLE:      Active Deep Densely Connected Convolutional Network for Hyperspectral Image Classification
http://arxiv.org/abs/2009.00320
AUTHORS:        Bing Liu ; Anzhu Yu ; Pengqiang Zhang ; Lei Ding ; Wenyue Guo ; Kuiliang Gao ; Xibing Zuo
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      It is still very challenging to use only a few labeled samples to train deep learning models to reach a high classification accuracy.

42, TITLE:      Uncovering Hidden Challenges in Query-Based Video Moment Retrieval
http://arxiv.org/abs/2009.00325
AUTHORS:        Mayu Otani ; Yuta Nakashima ; Esa Rahtu ; Janne Heikkilä
COMMENTS:       British Machine Vision Conference (BMVC), 2020 https://mayu-ot.github.io/hidden-challenges-MR
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we present a series of experiments assessing how well the benchmark results reflect the true progress in solving the moment retrieval task.

43, TITLE:      PIDNet: An Efficient Network for Dynamic Pedestrian Intrusion Detection
http://arxiv.org/abs/2009.00312
AUTHORS:        Jingchen Sun ; Jiming Chen ; Tao Chen ; Jiayuan Fan ; Shibo He
COMMENTS:       Proceedings of the 28th ACM International Conference on Multimedia (MM '20), October 12--16, 2020, Seattle, WA, USA
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose a novel and efficient multi-task deep neural network, PIDNet, to solve this problem.

44, TITLE:      Continuous Artificial Prediction Markets as a Syndromic Surveillance Technique
http://arxiv.org/abs/2009.00394
AUTHORS:        Fatemeh Jahedpari
CATEGORY:       cs.CY [cs.CY, cs.AI, cs.LG]
HIGHLIGHT:      In this paper, we discuss what are the challenges of syndromic surveillance systems and how continuous Artificial Prediction Market [Jahedpari et al., 2017] can effectively be applied to the problem of syndromic surveillance.

45, TITLE:      Suspect AI: Vibraimage, Emotion Recognition Technology, and Algorithmic Opacity
http://arxiv.org/abs/2009.00502
AUTHORS:        James Wright
CATEGORY:       cs.CY [cs.CY, cs.AI]
HIGHLIGHT:      I propose the term 'suspect AI' to describe the growing number of systems like vibraimage that algorithmically classify suspects / non-suspects, yet are themselves deeply suspect.

46, TITLE:      Reinforcement Learning-based Black-Box Evasion Attacks to Link Prediction in Dynamic Graphs
http://arxiv.org/abs/2009.00163
AUTHORS:        Houxiang Fan ; Binghui Wang ; Pan Zhou ; Ang Li ; Meng Pang ; Zichuan Xu ; Cai Fu ; Hai Li ; Yiran Chen
CATEGORY:       cs.CR [cs.CR, cs.AI, cs.LG, cs.SI]
HIGHLIGHT:      In this paper, we study the vulnerability of LPDG methods and propose the first practical black-box evasion attack.

47, TITLE:      Mapping Researchers with PeopleMap
http://arxiv.org/abs/2009.00091
AUTHORS:        Jon Saad-Falcon ; Omar Shaikh ; Zijie J. Wang ; Austin P. Wright ; Sasha Richardson ; Duen Horng ;  Chau
COMMENTS:       2020 IEEE Visualization
CATEGORY:       cs.DL [cs.DL, cs.CL, cs.HC]
HIGHLIGHT:      Directories routinely become outdated, and few help in visually summarizing researchers' work or supporting the exploration of shared interests among researchers.

48, TITLE:      Federated Edge Learning : Design Issues and Challenges
http://arxiv.org/abs/2009.00081
AUTHORS:        Afaf Taïk ; Soumaya Cherkaoui
COMMENTS:       Submitted to IEEE Network Magazine
CATEGORY:       cs.DC [cs.DC, cs.AI]
HIGHLIGHT:      In this article, we examine the existing challenges and trade-offs in Federated Edge Learning (FEEL).

49, TITLE:      PlotThread: Creating Expressive Storyline Visualizations using Reinforcement Learning
http://arxiv.org/abs/2009.00249
AUTHORS:        Tan Tang ; Renzhong Li ; Xinke Wu ; Shuhan Liu ; Johannes Knittel ; Steffen Koch ; Thomas Ertl ; Lingyun Yu ; Peiran Ren ; Yingcai Wu
COMMENTS:       to be published in IEEE VIS InfoVis 2020
CATEGORY:       cs.HC [cs.HC, cs.AI, J.5]
HIGHLIGHT:      In this work, we propose a reinforcement learning framework to train an AI agent that assists users in exploring the design space efficiently and generating well-optimized storylines.

50, TITLE:      Graph Embedding with Data Uncertainty
http://arxiv.org/abs/2009.00505
AUTHORS:        Firas Laakom ; Jenni Raitoharju ; Nikolaos Passalis ; Alexandros Iosifidis ; Moncef Gabbouj
COMMENTS:       20 pages, 4 figures
CATEGORY:       cs.LG [cs.LG, cs.AI, math.SP, stat.ML]
HIGHLIGHT:      In this paper, we propose to model artifacts in training data using probability distributions; each data point is represented by a Gaussian distribution centered at the original data point and having a variance modeling its uncertainty.

51, TITLE:      Unsupervised Domain Adaptation with Progressive Adaptation of Subspaces
http://arxiv.org/abs/2009.00520
AUTHORS:        Weikai Li ; Songcan Chen
CATEGORY:       cs.LG [cs.LG, cs.CV, stat.ML, 68T10, I.2.6]
HIGHLIGHT:      To overcome this, we propose a novel UDA method named Progressive Adaptation of Subspaces approach (PAS) in which we utilize such an intuition that appears much reasonable to gradually obtain reliable pseudo labels.

52, TITLE:      Advancing from Predictive Maintenance to Intelligent Maintenance with AI and IIoT
http://arxiv.org/abs/2009.00351
AUTHORS:        Haining Zheng ; Antonio R. Paiva ; Chris S. Gurciullo
COMMENTS:       The 3rd International Workshop on Artificial Intelligence of Things (AIoT) In conjunction with the 26th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining (KDD 2020)
CATEGORY:       cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT:      As Artificial Intelligent (AI) technology advances and increasingly large amounts of data become readily available via various Industrial Internet of Things (IIoT) projects, we evaluate the state of the art of predictive maintenance approaches and propose our innovative framework to improve the current practice.

53, TITLE:      Training Deep Neural Networks with Constrained Learning Parameters
http://arxiv.org/abs/2009.00540
AUTHORS:        Prasanna Date ; Christopher D. Carothers ; John E. Mitchell ; James A. Hendler ; Malik Magdon-Ismail
CATEGORY:       cs.LG [cs.LG, cs.CV, stat.ML, 68T07, 90C27, I.2.6]
HIGHLIGHT:      To this extent, we propose the Combinatorial Neural Network Training Algorithm (CoNNTrA), that leverages a coordinate gradient descent-based approach for training deep learning models with finite discrete learning parameters.

54, TITLE:      The Computational Capacity of Memristor Reservoirs
http://arxiv.org/abs/2009.00112
AUTHORS:        Forrest C. Sheldon ; Artemy Kolchinsky ; Francesco Caravelli
COMMENTS:       18 pages double columns
CATEGORY:       cs.NE [cs.NE, cond-mat.stat-mech, cs.ET, nlin.AO]
HIGHLIGHT:      We analyze the feasibility and optimal design of such reservoirs by considering the equations of motion of circuits that include both linear elements (resistors, inductors, and capacitors) and nonlinear memory elements (called memristors).

55, TITLE:      Adaptation in a System Metamodel for Evolutionary Computation
http://arxiv.org/abs/2009.00110
AUTHORS:        Patrik Christen ; Olivier Del Fabbro
COMMENTS:       10 pages, 1 figure
CATEGORY:       cs.NE [cs.NE]
HIGHLIGHT:      In this study we extend our system metamodel with the concept of adaption in order to integrate evolutionary computation in our so-called allagmatic method.

56, TITLE:      A Deep 2-Dimensional Dynamical Spiking Neuronal Network for Temporal Encoding trained with STDP
http://arxiv.org/abs/2009.00581
AUTHORS:        Matthew Evanusa ; Cornelia Fermuller ; Yiannis Aloimonos
CATEGORY:       cs.NE [cs.NE, cs.AI, cs.CV]
HIGHLIGHT:      We aim to show that precise timing of input stimulus is critical in forming synchronous neural groups in a layered network.

57, TITLE:      Formally Verified Transformation of Non-binary Constraints into Binary Constraints
http://arxiv.org/abs/2009.00583
AUTHORS:        Catherine Dubois
COMMENTS:       Part of WFLP 2020 pre-proceedings
CATEGORY:       cs.PL [cs.PL]
HIGHLIGHT:      In this paper we formalize this encoding in the proof assistant Coq and prove that any solution of the binary constraint satisfaction problem makes it possible to build a solution of the original problem and vice-versa.

58, TITLE:      Gaussian Process Gradient Maps for Loop-Closure Detection in Unstructured Planetary Environments
http://arxiv.org/abs/2009.00221
AUTHORS:        Cedric Le Gentil ; Mallikarjuna Vayugundla ; Riccardo Giubilato ; Wolfgang Stürzl ; Teresa Vidal-Calleja ; Rudolph Triebel
COMMENTS:       This work is accepted for presentation at the 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). Please find IEEE's copyright statement at the bottom of the first page. Cedric Le Gentil and Mallikarjuna Vayugundla share the first authorship of this paper
CATEGORY:       cs.RO [cs.RO, cs.CV]
HIGHLIGHT:      This paper presents a method to solve the loop closure problem using only spatial information.

59, TITLE:      Flightmare: A Flexible Quadrotor Simulator
http://arxiv.org/abs/2009.00563
AUTHORS:        Yunlong Song ; Selim Naji ; Elia Kaufmann ; Antonio Loquercio ; Davide Scaramuzza
CATEGORY:       cs.RO [cs.RO, cs.AI]
HIGHLIGHT:      In this work, we propose a paradigm-shift in the development of simulators: moving the trade-off between accuracy and speed from the developers to the end-users.

60, TITLE:      Twitter Corpus of the #BlackLivesMatter Movement And Counter Protests: 2013 to 2020
http://arxiv.org/abs/2009.00596
AUTHORS:        Salvatore Giorgi ; Sharath Chandra Guntuku ; Muhammad Rahman ; McKenzie Himelein-Wachowiak ; Amy Kwarteng ; Brenda Curtis
CATEGORY:       cs.SI [cs.SI, cs.CL]
HIGHLIGHT:      We introduce a data set of 41.8 million tweets from 10 million users which contain one of the following keywords: BlackLivesMatter, AllLivesMatter and BlueLivesMatter.

61, TITLE:      Data and Image Prior Integration for Image Reconstruction Using Consensus Equilibrium
http://arxiv.org/abs/2009.00092
AUTHORS:        Muhammad Usman Ghani ; W. Clem Karl
COMMENTS:       Submitted to IEEE Transactions on Computational Imaging
CATEGORY:       eess.IV [eess.IV, cs.CV, math.OC]
HIGHLIGHT:      In this work, a principled approach is presented allowing the unified integration of both data and image domain priors for improved image reconstruction.

62, TITLE:      Semantic Segmentation of Neuronal Bodies in Fluorescence Microscopy Using a 2D+3D CNN Training Strategy with Sparsely Annotated Data
http://arxiv.org/abs/2009.00029
AUTHORS:        Filippo Maria Castelli ; Matteo Roffilli ; Giacomo Mazzamuto ; Irene Costantini ; Ludovico Silvestri ; Francesco Saverio Pavone
CATEGORY:       eess.IV [eess.IV, cs.CV, q-bio.NC]
HIGHLIGHT:      Semi-supervised alternative strategies which make use only of sparse anno-tations suffer from longer training times and achieved models tend tohave increased capacity compared to 2D CNNs, needing more groundtruth data to attain similar results.

63, TITLE:      Image Super-Resolution using Explicit Perceptual Loss
http://arxiv.org/abs/2009.00382
AUTHORS:        Tomoki Yoshida ; Kazutoshi Akita ; Muhammad Haris ; Norimichi Ukita
COMMENTS:       9 pages, 5 figures
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      This paper proposes an explicit way to optimize the super-resolution network for generating visually pleasing images.

64, TITLE:      End-to-End Hyperspectral-Depth Imaging with Learned Diffractive Optics
http://arxiv.org/abs/2009.00463
AUTHORS:        Seung-Hwan Baek ; Hayato Ikoma ; Daniel S. Jeon ; Yuqi Li ; Wolfgang Heidrich ; Gordon Wetzstein ; Min H. Kim
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      In this work, we propose a monocular imaging system for simultaneously capturing hyperspectral-depth (HS-D) scene information with an optimized diffractive optical element (DOE).

65, TITLE:      Recognition Oriented Iris Image Quality Assessment in the Feature Space
http://arxiv.org/abs/2009.00294
AUTHORS:        Leyuan Wang ; Kunbo Zhang ; Min Ren ; Yunlong Wang ; Zhenan Sun
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      In this paper, we propose a recognition-oriented quality metric and assessment method for iris image to deal with the problem.

66, TITLE:      On The Usage Of Average Hausdorff Distance For Segmentation Performance Assessment: Hidden Bias When Used For Ranking
http://arxiv.org/abs/2009.00215
AUTHORS:        Orhun Utku Aydin ; Abdel Aziz Taha ; Adam Hilbert ; Ahmed A. Khalil ; Ivana Galinovic ; Jochen B. Fiebach ; Dietmar Frey ; Vince Istvan Madai
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      To mitigate this bias, we present a modified calculation of AVD that we have coined balanced AVD (bAVD). Adding the created errors consecutively and randomly to the ground truth, we created sets of simulated segmentations with increasing number of errors.

67, TITLE:      Image Reconstruction of Static and Dynamic Scenes through Anisoplanatic Turbulence
http://arxiv.org/abs/2009.00071
AUTHORS:        Zhiyuan Mao ; Nicholas Chimitt ; Stanley Chan
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      In this paper, we present a unified method for atmospheric turbulence mitigation in both static and dynamic sequences.

68, TITLE:      Quality-aware semi-supervised learning for CMR segmentation
http://arxiv.org/abs/2009.00584
AUTHORS:        Bram Ruijsink ; Esther Puyol-Anton ; Ye Li ; Wenja Bai ; Eric Kerfoot ; Reza Razavi ; Andrew P. King
COMMENTS:       MICCAI STACOM 2020
CATEGORY:       eess.IV [eess.IV, cs.CV, cs.LG]
HIGHLIGHT:      In this paper, we propose a novel scheme that uses QC of the downstream task to identify high quality outputs of CMR segmentation networks, that are subsequently utilised for further network training.



==========Updates to Previous Papers==========
1, TITLE:       MAP Inference for Probabilistic Logic Programming
http://arxiv.org/abs/2008.01394
AUTHORS:        Elena Bellodi ; Marco Alberti ; Fabrizio Riguzzi ; Riccardo Zese
COMMENTS:       Paper presented at the 36th International Conference on Logic Programming (ICLP 2020), University Of Calabria, Rende (CS), Italy, September 2020, 16 pages
CATEGORY:       cs.AI [cs.AI, cs.LO]
HIGHLIGHT:      In this paper, we consider two other important tasks in the PLP setting: the Maximum-A-Posteriori (MAP) inference task, which determines the most likely values for a subset of the random variables given evidence on other variables, and the Most Probable Explanation (MPE) task, the instance of MAP where the query variables are the complement of the evidence variables.

2, TITLE:       A Review of Emergency Incident Prediction, Resource Allocation and Dispatch Models
http://arxiv.org/abs/2006.04200
AUTHORS:        Ayan Mukhopadhyay ; Geoffrey Pettet ; Sayyed Vazirizade ; Di Lu ; Hiba Baroud ; Alex Jaimes ; Yevgeniy Vorobeychik ; Mykel Kochenderfer ; Abhishek Dubey
CATEGORY:       cs.AI [cs.AI, cs.CY, cs.DC]
HIGHLIGHT:      In this survey, we present models for incident prediction, resource allocation, and dispatch for emergency incidents.

3, TITLE:       Exploring Unknown States with Action Balance
http://arxiv.org/abs/2003.04518
AUTHORS:        Yan Song ; Yingfeng Chen ; Yujing Hu ; Changjie Fan
CATEGORY:       cs.AI [cs.AI, cs.LG]
HIGHLIGHT:      In this paper, we focus on improving the effectiveness of finding unknown states and propose action balance exploration, which balances the frequency of selecting each action at a given state and can be treated as an extension of upper confidence bound (UCB) to deep reinforcement learning.

4, TITLE:       Automatic Programming of Cellular Automata and Artificial Neural Networks Guided by Philosophy
http://arxiv.org/abs/1905.04232
AUTHORS:        Patrik Christen ; Olivier Del Fabbro
COMMENTS:       12 pages, 1 figure
CATEGORY:       cs.AI [cs.AI, cs.LG, cs.MA, cs.NE, cs.PL]
HIGHLIGHT:      Earlier we described a metamodel and its building blocks according to the philosophical concepts of structure (spatial dimension) and operation (temporal dimension).

5, TITLE:       Falcon 2.0: An Entity and Relation Linking Tool over Wikidata
http://arxiv.org/abs/1912.11270
AUTHORS:        Ahmad Sakor ; Kuldeep Singh ; Anery Patel ; Maria-Esther Vidal
COMMENTS:       CIKM 2020 Paper 8 pages
CATEGORY:       cs.CL [cs.CL]
HIGHLIGHT:      In this paper, we present Falcon 2.0, first joint entity, and relation linking tool over Wikidata.

6, TITLE:       What Can We Learn From Almost a Decade of Food Tweets
http://arxiv.org/abs/2007.05194
AUTHORS:        Uga Sproģis ; Matīss Rikters
CATEGORY:       cs.CL [cs.CL]
HIGHLIGHT:      We present the Latvian Twitter Eater Corpus - a set of tweets in the narrow domain related to food, drinks, eating and drinking.

7, TITLE:       AMBERT: A Pre-trained Language Model with Multi-Grained Tokenization
http://arxiv.org/abs/2008.11869
AUTHORS:        Xinsong Zhang ; Hang Li
CATEGORY:       cs.CL [cs.CL, cs.LG]
HIGHLIGHT:      In this paper, we propose a novel pre-trained language model, referred to as AMBERT (A Multi-grained BERT), on the basis of both fine-grained and coarse-grained tokenizations.

8, TITLE:       Temporal Mental Health Dynamics on Social Media
http://arxiv.org/abs/2008.13121
AUTHORS:        Tom Tabak ; Matthew Purver
CATEGORY:       cs.CL [cs.CL, cs.IR, cs.SI, I.2.7]
HIGHLIGHT:      We describe a set of experiments for building a temporal mental health dynamics system.

9, TITLE:       Interacting with Explanations through Critiquing
http://arxiv.org/abs/2005.11067
AUTHORS:        Diego Antognini ; Claudiu Musat ; Boi Faltings
COMMENTS:       Under review. 17 pages, 10 figures, 12 tables
CATEGORY:       cs.CL [cs.CL, cs.LG, stat.ML]
HIGHLIGHT:      We present a novel technique using aspect markers that learns to generate personalized explanations of recommendations from review texts, and we show that human users significantly prefer these explanations over those produced by state-of-the-art techniques.

10, TITLE:      Computing the Extremal Possible Ranks with Incomplete Preferences
http://arxiv.org/abs/2005.08962
AUTHORS:        Aviram Imber ; Benny Kimelfeld
COMMENTS:       arXiv admin note: substantial text overlap with arXiv:2002.09212
CATEGORY:       cs.GT [cs.GT, cs.CC]
HIGHLIGHT:      Computing the Extremal Possible Ranks with Incomplete Preferences

11, TITLE:      One Shot 3D Photography
http://arxiv.org/abs/2008.12298
AUTHORS:        Johannes Kopf ; Kevin Matzen ; Suhib Alsisan ; Ocean Quigley ; Francis Ge ; Yangming Chong ; Josh Patterson ; Jan-Michael Frahm ; Shu Wu ; Matthew Yu ; Peizhao Zhang ; Zijian He ; Peter Vajda ; Ayush Saraf ; Michael Cohen
COMMENTS:       Project page: https://facebookresearch.github.io/one_shot_3d_photography/ Code: https://github.com/facebookresearch/one_shot_3d_photography
CATEGORY:       cs.CV [cs.CV, cs.GR]
HIGHLIGHT:      In this work, we refer to a 3D photo as one that displays parallax induced by moving the viewpoint (as opposed to a stereo pair with a fixed viewpoint).

12, TITLE:      TIDE: A General Toolbox for Identifying Object Detection Errors
http://arxiv.org/abs/2008.08115
AUTHORS:        Daniel Bolya ; Sean Foley ; James Hays ; Judy Hoffman
COMMENTS:       Updated LVIS results with the v1.0.1 error calculation
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      We introduce TIDE, a framework and associated toolbox for analyzing the sources of error in object detection and instance segmentation algorithms.

13, TITLE:      Metrics for Exposing the Biases of Content-Style Disentanglement
http://arxiv.org/abs/2008.12378
AUTHORS:        Xiao Liu ; Spyridon Thermos ; Gabriele Valvano ; Agisilaos Chartsias ; Alison O'Neil ; Sotirios A. Tsaftaris
COMMENTS:       21 pages, 8 figures
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose metrics to measure how (un)correlated, biased, and informative the content and style representations are.

14, TITLE:      DeepSOCIAL: Social Distancing Monitoring and Infection Risk Assessment in COVID-19 Pandemic
http://arxiv.org/abs/2008.11672
AUTHORS:        Mahdi Rezaei ; Mohsen Azarmi
CATEGORY:       cs.CV [cs.CV, cs.LG, eess.IV, physics.med-ph]
HIGHLIGHT:      In this research, we develop a generic Deep Neural Network-Based model for automated people detection, tracking, and inter-people distances estimation in the crowd, using common CCTV security cameras.

15, TITLE:      Topological Sweep for Multi-Target Detection of Geostationary Space Objects
http://arxiv.org/abs/2003.09583
AUTHORS:        Daqi Liu ; Bo Chen ; Tat-Jun Chin ; Mark Rutten
COMMENTS:       12 pages, 12 figures, accepted to IEEE Transactions on Signal Processing
CATEGORY:       cs.CV [cs.CV, eess.IV]
HIGHLIGHT:      In this paper, we propose a novel multi-target detection technique based on topological sweep, to find GEO objects from a short sequence of optical images.

16, TITLE:      Discriminability Distillation in Group Representation Learning
http://arxiv.org/abs/2008.10850
AUTHORS:        Manyuan Zhang ; Guanglu Song ; Hang Zhou ; Yu Liu
COMMENTS:       To appear in Proceedings of the European Conference on Computer Vision (ECCV), 2020
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      This article provides a more essential and explicable view.

17, TITLE:      Visualizing Point Cloud Classifiers by Curvature Smoothing
http://arxiv.org/abs/1911.10415
AUTHORS:        Chen Ziwen ; Wenxuan Wu ; Zhongang Qi ; Li Fuxin
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose a novel approach to visualize features important to the point cloud classifiers.

18, TITLE:      ExplAIn: Explanatory Artificial Intelligence for Diabetic Retinopathy Diagnosis
http://arxiv.org/abs/2008.05731
AUTHORS:        Gwenolé Quellec ; Hassan Al Hajj ; Mathieu Lamard ; Pierre-Henri Conze ; Pascale Massin ; Béatrice Cochener
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we describe an eXplanatory Artificial Intelligence (XAI) that reaches the same level of performance as black-box AI, for the task of classifying Diabetic Retinopathy (DR) severity using Color Fundus Photography (CFP).

19, TITLE:      BSP-Net: Generating Compact Meshes via Binary Space Partitioning
http://arxiv.org/abs/1911.06971
AUTHORS:        Zhiqin Chen ; Andrea Tagliasacchi ; Hao Zhang
COMMENTS:       CVPR 2020 Best Student Paper Award. Project page: https://bsp-net.github.io, Code: https://github.com/czq142857/BSP-NET-original
CATEGORY:       cs.CV [cs.CV, cs.GR, cs.LG]
HIGHLIGHT:      Leading methods for learning generative models of shapes rely on implicit functions, and generate meshes only after expensive iso-surfacing routines.

20, TITLE:      Multimodal Image-to-Image Translation via Mutual Information Estimation and Maximization
http://arxiv.org/abs/2008.03529
AUTHORS:        Zhiwen Zuo ; Qijiang Xu ; Huiming Zhang ; Zhizhong Wang ; Haibo Chen ; Ailin Li ; Lei Zhao ; Wei Xing ; Dongming Lu
CATEGORY:       cs.CV [cs.CV, cs.LG, eess.IV, stat.ML]
HIGHLIGHT:      In this paper, we present a novel framework that can achieve multimodal image-to-image translation by simply encouraging the statistical dependence between the latent code and the output image in conditional generative adversarial networks.

21, TITLE:      A Unified Weight Learning and Low-Rank Regression Model for Robust Face Recognition
http://arxiv.org/abs/2005.04619
AUTHORS:        Miaohua Zhang ; Yongsheng Gao ; Jun Zhou
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we address this problem by a unified sparse weight learning and low-rank approximation regression model and applied it to the robust face recognition in the presence of varying types and levels of corruptions, such as random pixel corruptions and block occlusions, or disguise.

22, TITLE:      Prototype Mixture Models for Few-shot Semantic Segmentation
http://arxiv.org/abs/2008.03898
AUTHORS:        Boyu Yang ; Chang Liu ; Bohao Li ; Jianbin Jiao ; Qixiang Ye
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper, we propose prototype mixture models (PMMs), which correlate diverse image regions with multiple prototypes to enforce the prototype-based semantic representation.

23, TITLE:      Deep Atrous Guided Filter for Image Restoration in Under Display Cameras
http://arxiv.org/abs/2008.06229
AUTHORS:        Varun Sundar ; Sumanth Hegde ; Divya Kothandaraman ; Kaushik Mitra
COMMENTS:       To appear in ECCV 2020 RLQ Workshop. Supplementary material attached. For project website, see https://varun19299.github.io/deep-atrous-guided-filter/
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this work, we present Deep Atrous Guided Filter (DAGF), a two-stage, end-to-end approach for image restoration in UDC systems.

24, TITLE:      LabelEnc: A New Intermediate Supervision Method for Object Detection
http://arxiv.org/abs/2007.03282
AUTHORS:        Miao Hao ; Yitao Liu ; Xiangyu Zhang ; Jian Sun
COMMENTS:       To appear in ECCV 2020. Code is available at https://github.com/megvii-model/LabelEnc
CATEGORY:       cs.CV [cs.CV]
HIGHLIGHT:      In this paper we propose a new intermediate supervision method, named LabelEnc, to boost the training of object detection systems.

25, TITLE:      Key Points Estimation and Point Instance Segmentation Approach for Lane Detection
http://arxiv.org/abs/2002.06604
AUTHORS:        Yeongmin Ko ; Yunkwan Lee ; Shoaib Azam ; Farzeen Munir ; Moongu Jeon ; Witold Pedrycz
COMMENTS:       Submitted to "IEEE Transactions on Intelligent Transportation Systems"
CATEGORY:       cs.CV [cs.CV, cs.LG, eess.IV]
HIGHLIGHT:      To address these problems, in this paper, we propose a traffic line detection method called Point Instance Network (PINet); the method is based on the key points estimation and instance segmentation approach.

26, TITLE:      Suicidal Ideation Detection: A Review of Machine Learning Methods and Applications
http://arxiv.org/abs/1910.12611
AUTHORS:        Shaoxiong Ji ; Shirui Pan ; Xue Li ; Erik Cambria ; Guodong Long ; Zi Huang
COMMENTS:       IEEE Transactions on Computational Social Systems
CATEGORY:       cs.CY [cs.CY, cs.AI, cs.CL, cs.LG, cs.SI]
HIGHLIGHT:      This paper is the first survey that comprehensively introduces and discusses the methods from these categories.

27, TITLE:      Fast A* on Road Networks Using a Scalable Separator-Based Heuristic
http://arxiv.org/abs/1812.07441
AUTHORS:        Renjie Chen ; Craig Gotsman
CATEGORY:       cs.DS [cs.DS, cs.AI]
HIGHLIGHT:      We present a novel method to compute an effective admissible heuristic for the fastest-path travel time between two points on a road map, which can be used to significantly accelerate the classical A* algorithm when computing fastest paths.

28, TITLE:      RLScheduler: An Automated HPC Batch Job Scheduler Using Reinforcement Learning
http://arxiv.org/abs/1910.08925
AUTHORS:        Di Zhang ; Dong Dai ; Youbiao He ; Forrest Sheng Bao ; Bing Xie
COMMENTS:       14 pages; conference accepted
CATEGORY:       cs.DC [cs.DC, cs.AI, cs.LG]
HIGHLIGHT:      We present RLScheduler, a deep reinforcement learning based job scheduler for scheduling independent batch jobs in high-performance computing (HPC) environment.

29, TITLE:      Deep Reinforcement Learning Designed Shinnar-Le Roux RF Pulse using Root-Flipping: DeepRF_SLR
http://arxiv.org/abs/1912.09015
AUTHORS:        Dongmyung Shin ; Sooyeon Ji ; Doohee Lee ; Jieun Lee ; Se-Hong Oh ; Jongho Lee
COMMENTS:       Accepted at IEEE transactions on Medical Imaging (https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9174664)
CATEGORY:       cs.LG [cs.LG, cs.AI, eess.IV, eess.SP, stat.ML]
HIGHLIGHT:      A novel approach of applying deep reinforcement learning to an RF pulse design is introduced.

30, TITLE:      Ethical Adversaries: Towards Mitigating Unfairness with Adversarial Machine Learning
http://arxiv.org/abs/2005.06852
AUTHORS:        Pieter Delobelle ; Paul Temple ; Gilles Perrouin ; Benoît Frénay ; Patrick Heymans ; Bettina Berendt
COMMENTS:       15 pages, 3 figures, 1 table
CATEGORY:       cs.LG [cs.LG, cs.AI, stat.ML]
HIGHLIGHT:      We offer a framework that assists these groups in mitigating unfair representations stemming from the training datasets.

31, TITLE:      Machine learning for COVID-19 detection and prognostication using chest radiographs and CT scans: a systematic methodological review
http://arxiv.org/abs/2008.06388
AUTHORS:        Michael Roberts ; Derek Driggs ; Matthew Thorpe ; Julian Gilbey ; Michael Yeung ; Stephan Ursprung ; Angelica I. Aviles-Rivero ; Christian Etmann ; Cathal McCague ; Lucian Beer ; Jonathan R. Weir-McCall ; Zhongzhao Teng ; James H. F. Rudd ; Evis Sala ; Carola-Bibiane Schönlieb
COMMENTS:       25 pages, 3 figures, 2 tables
CATEGORY:       cs.LG [cs.LG, cs.CV, eess.IV, stat.ML]
HIGHLIGHT:      Methods: In this systematic review we reviewed EMBASE via OVID, MEDLINE via PubMed, bioRxiv, medRxiv and arXiv for published papers and preprints uploaded from Jan 1, 2020 to June 24, 2020.

32, TITLE:      A Generic Graph-based Neural Architecture Encoding Scheme for Predictor-based NAS
http://arxiv.org/abs/2004.01899
AUTHORS:        Xuefei Ning ; Yin Zheng ; Tianchen Zhao ; Yu Wang ; Huazhong Yang
COMMENTS:       14 pages main text; 10 pages appendix
CATEGORY:       cs.LG [cs.LG, cs.NE, stat.ML]
HIGHLIGHT:      This work proposes a novel Graph-based neural ArchiTecture Encoding Scheme, a.k.a. GATES, to improve the predictor-based neural architecture search.

33, TITLE:      Lossless CNN Channel Pruning via Gradient Resetting and Convolutional Re-parameterization
http://arxiv.org/abs/2007.03260
AUTHORS:        Xiaohan Ding ; Tianxiang Hao ; Ji Liu ; Jungong Han ; Yuchen Guo ; Guiguang Ding
COMMENTS:       9 pages, 7 figures
CATEGORY:       cs.LG [cs.LG, cs.CV, eess.IV, stat.ML]
HIGHLIGHT:      Inspired by the neurobiology research about the independence of remembering and forgetting, we propose to re-parameterize a CNN into the remembering parts and forgetting parts, where the former learn to maintain the performance and the latter learn for efficiency.

34, TITLE:      Gems of Corrado Böhm
http://arxiv.org/abs/1812.02243
AUTHORS:        Henk P. Barendregt
CATEGORY:       cs.LO [cs.LO, cs.PL, 03B40, 68N18, 68N20]
HIGHLIGHT:      Gems of Corrado Böhm

35, TITLE:      A reconstruction of the multipreference closure
http://arxiv.org/abs/1905.03855
AUTHORS:        Laura Giordano ; Valentina Gliozzi
COMMENTS:       57 pages
CATEGORY:       cs.LO [cs.LO, cs.AI, I.2.4]
HIGHLIGHT:      The paper describes a preferential approach for dealing with exceptions in KLM preferential logics, based on the rational closure.

36, TITLE:      System Metamodel Formalism
http://arxiv.org/abs/2005.01192
AUTHORS:        Patrik Christen
COMMENTS:       12 pages, 1 table
CATEGORY:       cs.NE [cs.NE, cs.AI, math.DS]
HIGHLIGHT:      In this study, a mathematical definition of the system metamodel and its model parameters is provided and the creation of concrete mathematical models, i.e. cellular automata and artificial neural networks, from it is proved.

37, TITLE:      Cybernetical Concepts for Cellular Automaton and Artificial Neural Network Modelling and Implementation
http://arxiv.org/abs/2001.02037
AUTHORS:        Patrik Christen ; Olivier Del Fabbro
COMMENTS:       12 pages, 1 figure
CATEGORY:       cs.OH [cs.OH, cs.AI, cs.NE]
HIGHLIGHT:      In this paper, we emphasise this allagmatic theory by showing how Simondon's philosophical concepts can be used to formulate a generic computer model or metamodel for complex systems modelling and its implementation in program code, according to generic programming.

38, TITLE:      LLOV: A Fast Static Data-Race Checker for OpenMP Programs
http://arxiv.org/abs/1912.12189
AUTHORS:        Utpal Bora ; Santanu Das ; Pankaj Kureja ; Saurabh Joshi ; Ramakrishna Upadrasta ; Sanjay Rajopadhye
COMMENTS:       Accepted in ACM TACO, August 2020
CATEGORY:       cs.PL [cs.PL, cs.LO, cs.SE, D.2; D.3]
HIGHLIGHT:      In this paper, we propose LLOV, a fast, lightweight, language agnostic, and static data race checker for OpenMP programs based on the LLVM compiler framework.

39, TITLE:      IR2Vec: LLVM IR based Scalable Program Embeddings
http://arxiv.org/abs/1909.06228
AUTHORS:        S. VenkataKeerthy ; Rohit Aggarwal ; Shalini Jain ; Maunendra Sankar Desarkar ; Ramakrishna Upadrasta ; Y. N. Srikant
COMMENTS:       Accepted in ACM TACO
CATEGORY:       cs.PL [cs.PL, cs.LG, cs.NE, cs.SE]
HIGHLIGHT:      We propose IR2Vec, a Concise and Scalable encoding infrastructure to represent programs as a distributed embedding in continuous space.

40, TITLE:      On Termination of Polynomial Programs with Equality Conditions
http://arxiv.org/abs/1510.05201
AUTHORS:        Yangjia Li ; Naijun Zhan ; Mingshuai Chen ; Hui Lu ; Guohua Wu ; Joost-Pieter Katoen
CATEGORY:       cs.PL [cs.PL, cs.SC, 68Q60 (Primary) 68W30, 68Q17 (Secondary)]
HIGHLIGHT:      We present an explicit recursive function which is essentially Ackermannian, to compute the maximal length of ascending chains of polynomial ideals under a control function, and thereby obtain a complete answer to the questions raised by Seidenberg.

41, TITLE:      Type-driven Neural Programming by Example
http://arxiv.org/abs/2008.12613
AUTHORS:        Kiara Grouwstra
CATEGORY:       cs.SE [cs.SE, cs.AI, cs.PL, I.2.2]
HIGHLIGHT:      We propose a way to incorporate programming types into a neural program synthesis approach for programming by example.

42, TITLE:      Patching as Translation: the Data and the Metaphor
http://arxiv.org/abs/2008.10707
AUTHORS:        Yangruibo Ding ; Baishakhi Ray ; Premkumar Devanbu ; Vincent J. Hellendoorn
CATEGORY:       cs.SE [cs.SE, cs.LG, cs.PL]
HIGHLIGHT:      In this work, we examine a common example of this phenomenon: the conceit that "software patching is like language translation". We will publicly release our replication data and materials at https://github.com/ARiSE-Lab/Patch-as-translation.

43, TITLE:      Parallel Rescoring with Transformer for Streaming On-Device Speech Recognition
http://arxiv.org/abs/2008.13093
AUTHORS:        Wei Li ; James Qin ; Chung-Cheng Chiu ; Ruoming Pang ; Yanzhang He
COMMENTS:       Proceedings of Interspeech, 2020
CATEGORY:       eess.AS [eess.AS, cs.CL]
HIGHLIGHT:      In this work we explore replacing the LSTM layers in the 2nd-pass rescorer with Transformer layers, which can process the entire hypothesis sequences in parallel and can therefore utilize the on-device computation resources more efficiently.

44, TITLE:      Thick Cloud Removal of Remote Sensing Images Using Temporal Smoothness and Sparsity-Regularized Tensor Optimization
http://arxiv.org/abs/2008.04529
AUTHORS:        Chenxi Duan ; Jun Pan ; Rui Li
CATEGORY:       eess.IV [eess.IV, cs.CV]
HIGHLIGHT:      In this paper, a novel thick cloud removal method for remote sensing images based on temporal smoothness and sparsity-regularized tensor optimization (TSSTO) is proposed.