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Extracting unknown repeated pattern in tiled images 19th

Extracting unknown repeated pattern in tiled images

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19th international conference on Hybrid Intelligent Systems, HIS 2019, and the 14th international conference on Information Assurance and Security, IAS 2019

作者： Neupane, Prasanga Tuladhar, Archana Sharma, Shreeniwas Tamang, Ravi Alternative Technology Kathmandu Nepal

ISBN: (纸本)9783030493356

Humans can easily recognize specific patterns and their repetition in an image but it is very difficult for machines to do. However, the machines can create a plethora of repeated patterns and tiled images. this research paper proposes some methods of recognizing an unknown repeated motif in tiled images in computer generated raster graphics. three approaches, autocorrelation of an image, comparing with template strip and cyclic bitwise XOR-ing of the image are compared in this paper. Finally, the third algorithm is proposed as it detects locally repeating unknown motifs in a tiled image and outperforms the former two methods in robustness and provides a reliable result. Unlike other traditional approaches, the proposed method does not require any feature extraction and clustering of features or patches and it is unsupervised. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Autocorrelation

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Explainable Hybrid CNN and FNN Approach Applied on Robotic Wall-Following Behaviour Learning 4

Explainable Hybrid CNN and FNN Approach Applied on Robotic W...

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4th international conference on Artificial Intelligence and pattern recognition, AIPR 2021

作者： Kwiatkowski, Jakub Ou, Liang Chang, Yu-Cheng Lin, Chin-Teng Institute of Computing Science Poznan University of Technology Poland School of Computer Science University of Technology Sydney Australia

ISBN: (纸本)9781450384087

Fuzzy Neural Network (FNN) applied to robotic control tasks has proved to be effective by previous researchers. However, FNN has an inherent deficiency in dealing with inputs of large dimensions, such as images. therefore, this research utilizes a Convolutional Neural Network (CNN) model to convert image into distance values and delivers these values to FNN based robot controller as inputs. the proposed hybrid CNN+FNN are tested with both a regression model and a multi-task model. Results show that the multi-task method performs better with less information loss from input images. this paper also proved that the proposed hybrid approach can be generalized into an unknown robotic simulation environment and performs better than its FNN counterpart. By utilizing state of the art explainable analysis method, both the CNN part and the FNN part of the hybrid approach can be explained in a human-understandable way. © 2021 ACM.

关键词： Fuzzy neural networks

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Acoustic pattern recognition Technology Based on the Viola-Jones Approach for VR and AR Systems 11th

Acoustic Pattern Recognition Technology Based on the Viola-J...

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11th Annual international conference on Brain-Inspired Cognitive Architectures for Artificial Intelligence, BICA*AI 2020

作者： Alyushin, Alexander M. Dvoryankin, Sergey V. Kashirskoe shosse 31 Moscow115409 Russia Plekhanov Russian University of Economics st. Hook 43 Moscow117997 Russia Financial University under the Government of the Russian Federation Leningradsky prospect 49 Moscow125993 Russia

ISBN: (纸本)9783030655952

the ability to solve problems of graphic images recognition in virtual reality (VR), augmented reality (AR), mixed reality (MR) and cross reality (XR) systems is highlighted as one of the most important. the urgency of solving problems of recognition and classification of acoustic images has been substantiated, which will bring the quality of VR, AR, MR and XR systems closer to real reality (RR). Independent solution of graphic and acoustic patterns recognition problems using heterogeneous algorithmic and software tools is attributed to the disadvantages of modern systems. the study proposes an approach that allows the use of unified methodological and software tools for the simultaneous solution of graphic and acoustic patterns recognition problems. the proposed approach is based on converting acoustic information into graphic information using 2D-images of dynamic sonograms. this allows the recognition of acoustic patterns using unified algorithmic and software tools. It is proposed to use the Viola-Jones technology as such a unified tool. It is shown that the implementation of a two-stage determination of similarity measures of primitives and areas of the original image makes it possible to increase the speed of algorithms. For this purpose, at the first iteration, it is proposed to use not the graphic primitives themselves, but their coordinate projections. In the study, by analogy with Haar’s features, parametrizable acoustic primitives were developed, presented in the classical graphical version, as well as in the form of coordinate projections. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Mixed reality

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Using 3D hahn moments as a computational representation of ats drugs molecular structure 11th

Using 3D hahn moments as a computational representation of a...

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11th international conference on Soft computing and pattern recognition, SoCPaR 2019, and 11th World Congress on Nature and Biologically Inspired computing, NaBIC 2019

作者： Pratama, Satrya Fajri Muda, Azah Kamilah Choo, Yun-Huoy Carbó-Dorca, Ramon Abraham, Ajith Research Group Center of Advanced Computing and Technologies Fakulti Teknologi Maklumat dan Komunikasi Universiti Teknikal Malaysia Melaka Hang Tuah Jaya Durian Tunggal Melaka76100 Malaysia Institut de Química Computacional i Catàlisi Universitat de Girona GironaCatalonia17071 Spain Scientific Network for Innovation and Research Excellence AuburnWA United States

ISBN: (纸本)9783030493448

the campaign against drug abuse is fought by all countries, most notably on ATS drugs. the technical limitations of the current test kits to detect new brand of ATS drugs present a challenge to law enforcement authorities and forensic laboratories. Meanwhile, new molecular imaging devices which allowed mankind to characterize the physical 3D molecular structure have been recently introduced, and it can be used to remedy the limitations of existing drug test kits. thus, a new type of 3D molecular structure representation technique should be developed to cater the 3D molecular structure acquired physically using these molecular imaging devices. One of the applications of image processing methods to represent a 3D image is 3D moments, and this study formulates a new 3D moments technique, namely 3D Hahn moments, to represent the 3D molecular structure of ATS drugs. the performance of the proposed technique was analysed using drug chemical structures obtained from UNODC for the ATS drugs, while non-ATS drugs are obtained randomly from ChemSpider database. the evaluation shows the technique is qualified to be further explored in the future works to be fully compatible with ATS drug identification domain. © the Editor(s) (if applicable) and the Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

关键词： Molecular structure

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Manual Evaluation Matters: Reviewing Test Protocols of Distantly Supervised Relation Extraction

Manual Evaluation Matters: Reviewing Test Protocols of Dista...

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Joint conference of 59th Annual Meeting of the Association-for-Computational-Linguistics (ACL) / 11th international Joint conference on Natural Language Processing (IJCNLP) / 6th Workshop on Representation Learning for NLP (RepL4NLP)

作者： Gao, Tianyu Han, Xu Bai, Yuzhuo Qiu, Keyue Xie, Zhiyu Lin, Yankai Liu, Zhiyuan Li, Peng Sun, Maosong Zhou, Jie Princeton Univ Dept Comp Sci Princeton NJ USA Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China WeChat AI Tencent Inc Pattern Recognit Ctr Beijing Peoples R China

ISBN: (纸本)9781954085541

Distantly supervised (DS) relation extraction (RE) has attracted much attention in the past few years as it can utilize large-scale auto-labeled data. However, its evaluation has long been a problem: previous works either take costly and inconsistent methods to manually examine a small sample of model predictions, or directly test models on auto-labeled data-which, by our check, produce as much as 53% wrong labels at the entity pair level in the popular NYT10 dataset. this problem has not only led to inaccurate evaluation, but also made it hard to understand where we are and what's left to improve in the research of DS-RE. To evaluate DS-RE models more credibly, we build manually-annotated test sets for two DS-RE datasets, NYT10 and Wiki20, and thoroughly evaluate several competitive models, especially the latest pre-trained ones. the experimental results show that the manual evaluation can indicate very different conclusions from automatic ones, especially some unexpected observations, e.g., pre-trained models can achieve dominating performance while being more susceptible to false-positives compared with previous methods. We hope that both our manual test sets and observations can help advance future DS-RE research.(1)

关键词： Extraction

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Texture Descriptor Extraction for Iris Classification using Fuzzy Logic Local Binary pattern 10

Texture Descriptor Extraction for Iris Classification using ...

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10th IEEE international conference on Control System, computing and Engineering, ICCSCE 2020

作者： Ali, Ahmad N. Universiti Sains Malaysia School of Electrical and Electronic Engineering Engineering Campus Penang Malaysia

ISBN: (纸本)9781728172439

Feature extraction in the iris classification field is an essential stage that is applied to produce a discriminative descriptor for each subject. Uncertainty due to contrast and other noises may degrade the exactness of descriptors between subject. In achieving to tackle the problem, fuzzy logic local binary pattern concept that uses a specific rule to decide the uncertainty intensity values between central and neighboring pixels has been applied for feature extraction. An experimental setup has been performed to evaluate the concept using two freely available databases which are CASIA V3 and IITD. A promising result with more than 98% of the classification score success rate has achieved by using Support Vector Machines classifier. © 2020 IEEE.

关键词： Support vector machines

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10th international conference on Network Games, Control and Optimization, NETGCOOP 2020

10th International Conference on Network Games, Control and ...

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10th international conference on Network Games, Control and Optimization, NETGCOOP 2020

ISBN: (纸本)9783030874728

the proceedings contain 28 papers. the special focus in this conference is on Network Games, Control and Optimization. the topics include: Unlicensed Spectrum for Ultra-Reliable Low-Latency Communication in Multi-tenant Environment;spectrum Sharing Secondary Users in Presence of Multiple Adversaries;dynamic Bus Dispatch Policies;multi Objective Decision Making for Virtual Machine Placement in Cloud computing;a Markovian Approach for Improving End-to-End Data Rates in the Internet;DNN Based Beam Selection in mmW Heterogeneous Networks;A Bilevel Model for Centralized Optimization of Charging Stops for EV on Highways;coordinated Scheduling Based on Automatic Neighbor Beam Relation;financial Replicator Dynamics: Emergence of Systemic-Risk-Averting Strategies;A Game-theoretical Approach for Energy Efficiency in Multiuser MIMO System;impact of Private Observation in the Bayesian Persuasion Game;optimal Campaign Strategy for Social Media Marketing with a Contrarian Population;a Dynamic Game Formulation for Control of Opinion Dynamics over Social Networks;opinion Dynamics with Multi-body Interactions;modeling Limited Attention in Opinion Dynamics by Topological Interactions;optimal Pricing Approach Based on Expected Utility Maximization with Partial Information;price Formation and Optimal Trading in Intraday Electricity Markets;Distributed Static State Feedback Control for DC Microgrids;design of a Combinatorial Double Auction for Local Energy Markets;Derivative-Free Optimization over Multi-user MIMO Networks;a Friendly Interference Game in Wireless Secret Communication Networks;slow-Link Adaptation Algorithm for Multi-source Multi-relay Wireless Networks Using Best-Response Dynamics;vaccination in a Large Population: Mean Field Equilibrium Versus Social Optimum;controlling Packet Drops to Improve Freshness of Information;maximizing Amount of Transferred Traffic for Battery Powered Mobiles.

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Interactive, Efficient and Creative Image Generation Using Compositional pattern-Producing Networks 10th

Interactive, Efficient and Creative Image Generation Using C...

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10th international conference on Artificial Intelligence in Music, Sound, Art and Design, EvoMUSART 2021 held as Part of EvoStar 2021

作者： Ekern, Erlend Gjesteland Gambäck, Björn Department of Computer Science Norwegian University of Science and Technology Trondheim7491 Norway Digital Systems RISE Research Institute of Sweden AB Kista164 29 Sweden

ISBN: (纸本)9783030729134

In contrast to most recent models that generate an entire image at once, the paper introduces a new architecture for generating images one pixel at a time using a Compositional pattern-Producing Network (CPPN) as the generator part in a Generative Adversarial Network (GAN), allowing for effective generation of visually interesting images with artistic value, at arbitrary resolutions independent of the dimensions of the training data. the architecture, as well as accompanying (hyper-) parameters, for training CPPNs using recent GAN stabilisation techniques is shown to generalise well across many standard datasets. Rather than relying on just a latent noise vector (entangling various features with each other), mutual information maximisation is utilised to get disentangled representations, removing the requirement to use labelled data and giving the user control over the generated images. A web application for interacting with pre-trained models was also created, unique in the offered level of interactivity with an image-generating GAN. © 2021, Springer Nature Switzerland AG.

关键词： Generative adversarial networks

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Explainable Model Selection of a Convolutional Neural Network for Driver’s Facial Emotion Identification 25th

Explainable Model Selection of a Convolutional Neural Networ...

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25th international conference on pattern recognition Workshops, ICPR 2020

作者： Kandeel, Amany A. Abbas, Hazem M. Hassanein, Hossam S. School of Computing Queen’s University Kingston Canada Department Computer and Systems Engineering Ain Shams University Cairo Egypt

ISBN: (纸本)9783030687793

Road accidents have a significant impact on increasing death rates. In addition to weather, roads, and vehicles, human error constitutes these accidents’ main reason. So, driver-safety technology is one of the common research areas, whether in academia or industry. the driver’s behavior is influenced by his feelings such as anger or sadness, as well as the physical distraction factors such as using mobile or drinking. recognition of the driver’s emotions is crucial in expecting the driver’s behavior and dealing with it. In this work, the Convolutional Neural Network (CNN) model is employed to implement a Facial Expression recognition (FER) approach to identify the driver’s emotions. the proposed CNN model has achieved considerable performance in prediction and classification tasks. However, it is similar to other deep learning approaches that have a lack of transparency and interpretability. We use Explainable Artificial Intelligence (XAI) techniques that generate interpretations for decisions and provide human-explainable representations to address this shortage. We utilize two visualization methods of XAI approaches to support our decision of choosing the architecture of the proposed FER model. Our model achieves accuracies of 92.85%, 99.28%, 88.88%, and 100% for the JAFFE, CK+, KDEF, and KMU-FED datasets, respectively. © 2021, Springer Nature Switzerland AG.

关键词： Convolution

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10th international conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2020

10th International Conference in Methodologies and Intellige...

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10th international conference in Methodologies and Intelligent Systems for Technology Enhanced Learning, MIS4TEL 2020

ISBN: (纸本)9783030522865

the proceedings contain 26 papers. the special focus in this conference is on Methodologies and Intelligent Systems for Technology Enhanced Learning. the topics include: Evaluating statistical and informatics competencies in medical students in a blended learning course;a serious game and negotiation skills in nursing students: A pilot study;preface;construction of fuzzy-classification expert system in cerebral palsy for learning performance facilitation;interprofessional high-fidelity simulation on nursing students’ collaborative attitudes: A quasi-experimental study using a mixed-methods approach;from high-fidelity patient simulators to robotics and artificial intelligence: A discussion paper on new challenges to enhance learning in nursing education;the concept of high-fidelity simulation and related factors in nursing education: A scoping review;the use of simulation for teaching therapy management: An observational descriptive study on 2nd and 3rd year students of the nursing degree course of Reggio Emilia;computer laboratory: the key to access the electronic databases in learning evidence-based practice;perspectives in nursing education: From paper standardized taxonomies to electronic records applied in nursing practice;the perceived usefulness of a problem-solving incorporated into blended learning in nursing education: A descriptive study;Authoring interactive-video exercises with ELEVATE: the NLS procedure case study;extending and evaluating a collaborative note-taking application: A pilot study;Enhancing learning opportunities for CS: Experiences from two learning systems;security in multimedia information systems: Analysis and prediction;a dynamic recommender system for online judges based on autoencoder neural networks;lessons learned from implementing blended learning for classes of different size;smart object design by children as protagonists.

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