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检索条件"任意字段=2021 10th International Conference on Computing and Pattern Recognition"

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Jointly Learning Multiple Curvature Descriptor for 3D Palmprint recognition

Jointly Learning Multiple Curvature Descriptor for 3D Palmpr...

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international conference on pattern recognition

作者： Lunke Fei Jianyang Qin Peng Liu Jie Wen Chunwei Tian Bob Zhang Shuping Zhao School of Computer Science and Technology Guangdong University of Technology Guangzhou China The Bio-Computing Research Center Harbin Institute of Technology(Shenzhen) Shenzhen China University of Macau Taipa Macau China

3D palmprint-based biometric recognition has drawn growing research attention due to its several merits over 2D counterpart such as robust structural measurement of a palm surface and high anti-counterfeiting capability. However, most existing 3D palmprint descriptors are hand-crafted that usually extract stationary features from 3D palmprint images. In this paper, we propose a feature learning method to jointly learn compact curvature feature descriptor for 3D palmprint recognition. We first form multiple curvature data vectors to completely sample the intrinsic curvature information of 3D palmprint images. then, we jointly learn a feature projection function that project curvature data vectors into binary feature codes, which have the maximum inter-class variances and minimum intra-class distance so that they are discriminative. Moreover, we learn the collaborative binary representation of the multiple curvature feature codes by minimizing the information loss between the final representation and the multiple curvature features, so that the proposed method is more compact in feature representation and efficient in matching. Experimental results on the baseline 3D palmprint database demonstrate the superiority of the proposed method in terms of recognition performance in comparison with state-of-the-art 3D palmprint descriptors.

关键词： three-dimensional displays Palmprint recognition Databases Collaboration Binary codes Feature extraction pattern recognition

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international Joint conference: 12th international conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th international conference on European Transnational Education, ICEUTE 2019

International Joint Conference: 12th International Conferenc...

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international Joint conference: 12th international conference on Computational Intelligence in Security for Information Systems, CISIS 2019 and 10th international conference on European Transnational Education, ICEUTE 2019

ISBN: (纸本)9783030200046

the proceedings contain 35 papers. the special focus in this conference is on Computational Intelligence in Security for Information Systems. the topics include: Deep Wavelet Extreme Learning Machine for Data Classification;palm Vein Age and Gender Estimation Using Center Symmetric-Local Binary pattern;neuro-Evolutionary Feature Selection to Detect Android Malware;improving Blockchain Security Validation and Transaction Processing through Heterogeneous computing;anomaly Detection on Patients Undergoing General Anesthesia;an Internet Voting Proposal Towards Improving Usability and Coercion Resistance;linearization of Cryptographic Sequences;On-the-Fly Testing an Implementation of Arrow Lightweight PRNG Using a LabVIEW Framework;a Novel Construction for Streaming Networks with Delays;comparison of System Call Representations for Intrusion Detection;hyot: Leveraging Hyperledger for Constructing an Event-Based Traceability System in IoT;data Mining for Statistical Evaluation of Summative and Competency-Based Assessments in Mathematics;looking for the Antidote for Contaminated Water: Learning through an Escape Game;enhancing Collaborative Learning through Pedagogical Alignment;rules_Math: Establishing Assessment Standards;evaluate Mathematical Competencies in Engineering Using Video-Lessons;statistical Methods – What Is the Problem Here?;enhancing the Learning of Cryptography through Concept Maps;analysis of Student Achievement Scores: A Machine Learning Approach;analysis of Teacher Training in Mathematics in Paraguay’s Elementary Education System Using Machine Learning Techniques;Efficient Verification of Security Protocols Time Properties Using SMT Solvers;cooperative Evaluation Using Moodle;automatic Categorization of Introductory Programming Students;using Scratch to Improve Undergraduate Students’ Skills on Artificial Intelligence;project-Based Methodology to Lecture on Web Frameworks Applied to the Management of Health-Related Data;mirkwood: An Online Parallel Craw

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Explainable Hybrid CNN and FNN Approach Applied on Robotic Wall-Following Behaviour Learning 21

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

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Proceedings of the 2021 4th international conference on Artificial Intelligence and pattern recognition

作者： Jakub Kwiatkowski Liang Ou Yu-Cheng Chang Chin-Teng Lin 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.

关键词： Additional Key Words and Phrases: Explainable AI Fuzzy System Robotic Navigation

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An Analysis to Empower IoT Devices through FOG computing

An Analysis to Empower IoT Devices through FOG Computing

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Annual Information Technology, Electromechanical Engineering and Microelectronics conference (IEMECON)

作者： Jyoti Anand Jyoti Khandelwal Deepak Sethi Shivam Choudhary Computer Science Engineering University of Engineering & Management Jaipur India Computer Science Engineering SRM University Delhi-NCR India

ISBN: (纸本)9781665426879

Increasing numbers of physical IoT devices to the internet generates huge amount of data, further need to communicate to the preferred locality. Today, IoT has become an integral part of the people's regular routine. Process, store and communicate the sensory data to the distant cloud center causes to be network congestion, high latency and power consumption. It couldn't provide real time data analysis in critical scenarios like e-healthcare, military, smart traffic system etc. also. Using fog computing (FC) between cloud layer and end user devices is a way for empowering the IoT devices and provide real time feedback. It's not a substitute for cloud, just enables data processing tasks at the edge of network. An offloading mechanism is used in FC to improve the lifetime of power constraints IoT devices. In this paper, we first facilitate a brief explanation about FC, its common framework, characteristics and challenges. then a comprehensive survey of existing offloading algorithms of FC is provided with their scope and benefits.

关键词： Cloud computing Energy consumption Machine learning algorithms Data analysis Power demand Real-time systems Internet of things

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Improved Residual Networks for Image and Video recognition

Improved Residual Networks for Image and Video Recognition

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international conference on pattern recognition

作者： Ionut Cosmin Duta Li Liu Fan Zhu Ling Shao Inception Institute of Artificial Intelligence (IIAI) UAE

Residual networks (ResNets) represent a powerful type of convolutional neural network (CNN) architecture, widely adopted and used in various tasks. In this work we propose an improved version of ResNets. Our proposed improvements address all three main components of a ResNet: the flow of information through the network layers, the residual building block, and the projection shortcut. We are able to show consistent improvements in accuracy and learning convergence over the baseline. For instance, on ImageNet dataset, using the ResNet with 50 layers, for top-1 accuracy we can report a 1.19% improvement over the baseline in one setting and around 2% boost in another. Importantly, these improvements are obtained without increasing the model complexity. Our proposed approach allows us to train extremely deep networks, while the baseline shows severe optimization issues. We report results on three tasks over six datasets: image classification (ImageNet, CIFAR-10 and CIFAR-100), object detection (COCO) and video action recognition (Kinetics-400 and Something-Something-v2). In the deep learning era, we establish a new milestone for the depth of a CNN. We successfully train a 404-layer deep CNN on the ImageNet dataset and a 3002-layer network on CIFAR-10 and CIFAR-100, while the baseline is not able to converge at such extreme depths. Code and models are publicly available at: https://***/iduta/iresnet.

关键词： Training Image recognition Object detection Distance measurement Complexity theory pattern recognition Task analysis

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thwarting Cyber Crime and Phishing Attacks with Machine Learning: A Study

Thwarting Cyber Crime and Phishing Attacks with Machine Lear...

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international conference on Advanced computing and Communication Systems (ICACCS)

作者： M Arshey K S Angel Viji Noorul Islam Centre for Higher Education Thuckalay College of Engineering Kidangoor

Almost over 4 billion people are currently making rampant usage of the internet. the massive utilization of mobile technology along with the rise of the digital era caused a socio-technical threat to the Government and to the public. Many new developments in the internet and modern technologies give rise to new illegal and unethical opportunities among which some of them are crime. Cyber crime is an unlawful means which makes use of a digital media either as a tool or as a target or both. Cyber crime cases, which includes mainly the Phishing attacks and many other attacks in the prevailing COVID -19 situation, have reached an alarming rate with the outburst of numerous forms of crime. this paper focuses on various types of cyber crime and targets some of the present day cyber crime attacks based on Phishing, Artificial Intelligence, Cloud technology and Block chain. the principal objective of this work is to identify how Machine Learning can be deployed in detection of diversified fields of cyber crime. the application of various Machine Learning models in the prediction, identification and mitigation of complex threats is also discussed.

关键词： COVID-19 Phishing Machine learning Tools Predictive models pattern recognition Security

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Let's Play Music: Audio-Driven Performance Video Generation

Let's Play Music: Audio-Driven Performance Video Generation

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international conference on pattern recognition

作者： Hao Zhu Yi Li Feixia Zhu Aihua Zheng Ran He Center for Research on Intelligent Perception and Computing (CRIPAC) National Laboratory of Pattern Recognition (NLPR) CASIA Beijing China Center for Excellence in Brain Science and Intelligence Technology CAS Beijing China Anhui Provincial Key Laboratory of Multimodal Cognitive Computation School of Computer Science and Technology Anhui University

We propose a new task named Audio-driven Performance Video Generation (APVG), which aims to synthesize the video of a person playing a certain instrument guided by a given music audio clip. It is a challenging task to generate the high-dimensional temporal consistent videos from low-dimensional audio modality. In this paper, we propose a multi-staged framework to generate realistic and synchronized performance video from given music. Firstly, we provide both global appearance and local spatial information by generating the coarse videos and keypoints of body and hands from a given music respectively. then, we propose to transform the generated keypoints to heatmap via a differentiable space transformer, since the heatmap provides more spatial information but is harder to generate directly from audio. Finally, we propose a Structured Temporal UNet (STU) to extract both intra-frame structured information and interframe temporal consistency. they are obtained via graph-based structure module, and CNN-GRU based high-level temporal module respectively for final video generation. Comprehensive experiments validate the effectiveness of our proposed framework.

关键词： Instruments Transforms Synchronization Data mining Task analysis Space heating

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Unsupervised 3D Human Pose Estimation in Multi-view-multi-pose Video

Unsupervised 3D Human Pose Estimation in Multi-view-multi-po...

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international conference on pattern recognition

作者： Cheng Sun Diego thomas Hiroshi Kawasaki Kyushu University

3D human pose estimation from a single 2D video is an extremely difficult task because computing 3D geometry from 2D images is an ill-posed problem. Recent popular solutions adopt fully-supervised learning strategy, which requires to train a deep network on a large-scale ground truth dataset of 3D poses and 2D images. However, such a large-scale dataset with natural images does not exist, which limits the usability of existing methods. While building a complete 3D dataset is tedious and expensive, abundant 2D in-the-wild data is already publicly available. As a consequence, there is a growing interest in the computer vision community to design efficient techniques that use the unsupervised learning strategy, which does not require any ground truth 3D data. Such methods can be trained with only natural 2D images of humans. In this paper we propose an unsupervised method for estimating 3D human pose in videos. the standard approach for unsupervised learning is to use the Generative Adversarial Network (GAN) framework. To improve the performance of 3D human pose estimation in videos, we propose a new GAN network that enforces body consistency over frames in a video. We evaluate the efficiency of our proposed method on a public 3D human body dataset.

关键词： Geometry Computer vision three-dimensional displays Pose estimation Generative adversarial networks pattern recognition Usability

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GCNs-Based Context-Aware Short Text Similarity Model

GCNs-Based Context-Aware Short Text Similarity Model

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international conference on pattern recognition

作者： Xiaoqi Sun Shaochun Wu Yue Liu School of Computer Engineering and Science Shanghai University School of Computer Engineering and Science Shanghai University Shanghai Institute for Advanced Communication and Data Science Shanghai Engineering Research Center of Intelligent Computing System Shanghai China

ISBN: (纸本)9781728188089;9781728188096

Semantic textual similarity is a fundamental task in text mining and natural language processing (NLP), which has profound research value. the essential step for text similarity is text representation learning. Recently, researches have explored the graph convolutional network (GCN) techniques on text representation, since GCN does well in handling complex structures and preserving syntactic information. However, current GCN models are usually limited to very shallow layers due to the vanishing gradient problem, which can not capture non-local dependency information of sentences. In this paper, we propose a GCNs-based context-aware (GCSTS) model that applies iterated GCN blocks to train deeper GCNs. Recurrently employing the same GCN block prevents over-fitting and provides broad effective input width. Combined with dense connections, GCSTS can be trained more deeply. Besides, we use dynamic graph structures in the block, which further extends the receptive field of each vertex in graphs, learning better sentence representations. Experiments show that our model outperforms existing models on several text similarity datasets, while also verify that GCNs-based text representation models can be trained in a deeper manner, rather than being trained in two or three layers.

关键词： Text mining Semantics Syntactics Natural language processing pattern recognition Task analysis Context modeling

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Adaptive Central pattern Generator to Control a Modular Lower Limb Rehabilitation Exoskeleton

Adaptive Central Pattern Generator to Control a Modular Lowe...

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international IEEE/EMBS conference on Neural Engineering, CNE

作者： Alberto Plaza Mar Hernandez Jaime Ramos Gonzalo Puyuelo Elena Garces Elena Garcia Marsi Bionics and Universidad Politecnica de Madrid Madrid Spain Centro de Automática y Robótica CSIC Madrid Spain Marsi Bionics Madrid Spain

One of the main challenges of current exoskeletons is the adaptation to the patients' walking capabilities. the proposal of a new device made from modular joints actuators offers new ways of adaptability. However, it also requires changes in control techniques. In this contribution, we introduce the use of a central pattern generator (CPG) based on adaptive Hopf oscillators in order to achieve the decentralised architecture for the modular exoskeleton. Hebbian learning algorithm is used to train the Adaptive CPG, using the data collected from a healthy subject's gait to mimic the human walking trajectories on the hip and knee. the analysis shows how the oscillator manages to learn the training signal and proves that once the oscillator has completed the learning process it no longer needs this signal. Furthermore, its modulation properties of amplitude and frequency are demonstrated, turning out to be suitable in the exoskeletons' control. Finally, results show how the algorithm is tested in two different configurations of the modular exoskeleton prototype, confirming its functioning.

关键词： Legged locomotion Knee Training Actuators Exoskeletons Prototypes Turning

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