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German Microwave Conference

作者： Daniel Seyfried André Busche Ruth Janning Lars Schmidt-Thieme Joerg Schoebel Institute for High-Frequency Technology (IHF) Technische Universitäat Braunschweig Brunswick Germany Information Systems and Machine Learning Laboratory (ISMLL) University of Hildesheim Hildesheim Germany

To detect and characterize pipes and cables buried in the ground and to track their course we propose a new approach, which consists of an ultrawideband radar system employed as Ground Penetrating Radar (GPR) and a machine learning algorithm for the objects' hyperbola identification and evaluation directly in the recorded radargram.

关键词： Ground penetrating radar machine learning algorithms machine learning Generators Antenna measurements Noise

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Analysis of the Research Status of information Technology Education Literature in China and Abroad 24

Analysis of the Research Status of Information Technology Ed...

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24th IEEE International Conference on High Performance Computing and Communications, 8th IEEE International Conference on Data Science and systems, 20th IEEE International Conference on Smart City and 8th IEEE International Conference on Dependability in Sensor, Cloud and Big Data systems and Application, HPCC/DSS/SmartCity/DependSys 2022

作者： Zhao, Yulin Liu, Kai Li, Junke School of Information Engineering Suqian University Jiangsu Suqian223800 China School of Computer and Information Qiannan Normal University for Nationalities Duyun558000 China Jiangsu Province Engineering Research Center of Smart Poultry Farming and Intelligent Equipment Jiangsu 223800 China Key Laboratory of Machine Learning and Unstructured Data Processing of Qiannan Duyun558000 China Key Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun558000 China

ISBN: (纸本)9798350319934

information technology education contributes to the development of national education. Understanding the research status of information technology education at home and abroad is helpful to the implementation of education informatization. This paper takes the information technology education research literature collected by CNKI and the Web of Science database as the object and uses the bibliometric method to analyze the literature characteristics of information technology education at home and abroad. Specifically, the paper mainly carries out statistical analysis from the time distribution of literature, journal distribution, and hot keywords, and visually describes the literature features of the information technology education discipline, providing references for the research of information technology education discipline at home and abroad. © 2022 IEEE.

关键词： Informatization

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Emerging Intelligent Computing Technology and Applications 1

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丛书名： Communications in Computer and information Science

1000年

作者： De-Shuang Huang Phalguni Gupta Ling Wang Michael Gromiha

ISBN: (数字)9783642396786

ISBN: (纸本)9783642396779

This book constitutes the refereed proceedings of the 9th International Conference on Intelligent Computing, ICIC 2013, held in Nanning, China, in July 2013. The 192 revised full papers presented in the three volumes LNCS 7995, LNAI 7996, and CCIS 375 were carefully reviewed and selected from 561 submissions. The papers in this volume (CCIS 375) are organized in topical sections on Neural Networks; systems Biology and Computational Biology; Computational Genomics and Proteomics; Knowledge Discovery and Data Mining; Evolutionary learning and Genetic Algorithms; machine learning Theory and Methods; Biomedical Informatics Theory and Methods; Particle Swarm Optimization and Niche Technology; Unsupervised and Reinforcement learning; Intelligent Computing in Bioinformatics; Intelligent Computing in Finance/Banking; Intelligent Computing in Petri Nets/Transportation systems; Intelligent Computing in Signal Processing; Intelligent Computing in Pattern Recognition; Intelligent Computing in Image Processing; Intelligent Computing in Robotics; Intelligent Computing in Computer Vision; Special Session on Biometrics System and Security for Intelligent Computing; Special Session on Bio-inspired Computing and Applications; Computer Human Interaction using Multiple Visual Cues and Intelligent Computing; Special Session on Protein and Gene Bioinformatics: Analysis, Algorithms and Applications.

关键词： Mathematical Models of Cognitive Processes and Neural Networks Artificial Intelligence Computational Biology/Bioinformatics Image Processing and Computer Vision Pattern Recognition Biometrics

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Retraction Note: Association Strategies of Speech Behavior of Communicators in Coding Discourse: An Interdisciplinary Approach Towards Understanding the Role of Cognitive and Linguistic Processes in Communication

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Journal of psycholinguistic research 2025年第2期54卷 20页

作者： Ludan Zhang Irina Karabulatova Artem Nurmukhametov Margarita Lagutkina Department of Foreign Languages of Philological Faculty Peoples Friendship University of Russia Named after Patrice Lumumba RUDN University) Moscow Russia. Department of Foreign Languages of Philological Faculty Peoples Friendship University of Russia Named after Patrice Lumumba RUDN University) Moscow Russia. radogost2000@mail.ru. Laboratory of Machine Learning and Semantic Analysis Institute of Artificial Intelligence Lomonosov Moscow State University Moscow Russia. radogost2000@mail.ru. Department of Machine Learning and Digital Humanities Moscow Institute of Physics and Technology (National University) Dolgoprudny Russia. radogost2000@mail.ru. Department of Information Processing and Control Systems Bauman Moscow State Technological University Moscow Russia. radogost2000@mail.ru. Institute of Humanities Yugra State University Khanty-Mansiysk Russia.

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Universal gradient methods for stochastic convex optimization 24

Universal gradient methods for stochastic convex optimizatio...

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Proceedings of the 41st International Conference on machine learning

作者： Anton Rodomanov Ali Kavis Yongtao Wu Kimon Antonakopoulos Volkan Cevher CISPA Helmholtz Center for Information Security Saarbrücken Germany Institute for Foundations of Machine Learning (IFML) UT Austin Texas Laboratory for Information and Inference Systems (LIONS) EPFL Lausanne Switzerland

We develop universal gradient methods for Stochastic Convex Optimization (SCO). Our algorithms automatically adapt not only to the oracle's noise but also to the Hölder smoothness of the objective function without a priori knowledge of the particular setting. The key ingredient is a novel strategy for adjusting step-size coefficients in the Stochastic Gradient Method (SGD). Unlike AdaGrad, which accumulates gradient norms, our Universal Gradient Method accumulates appropriate combinations of gradient- and iterate differences. The resulting algorithm has state-of-the-art worst-case convergence rate guarantees for the entire Hölder class including, in particular, both nonsmooth functions and those with Lipschitz continuous gradient. We also present the Universal Fast Gradient Method for SCO enjoying optimal efficiency estimates.

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Stochastic Frank-Wolfe Methods for Nonconvex Optimization

Stochastic Frank-Wolfe Methods for Nonconvex Optimization

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Annual Allerton Conference on Communication, Control, and Computing

作者： Sashank J. Reddi Suvrit Sra Barnabás Póczos Alex Smola Machine Learning Department Carnegie Mellon University Pittsburgh PA 15213 USA the Laboratory for Information and Decision Systems Massachusetts Institute of Technology Cambridge MA 02139 USA

ISBN: (纸本)9781509045518

We study Frank-Wolfe methods for nonconvex stochastic and finite-sum optimization problems. Frank-Wolfe methods (in the convex case) have gained tremendous recent interest in machine learning and optimization due to their projection-free property and their ability to exploit structured constraints. However, our understanding of these algorithms in the nonconvex setting is fairly limited. In this paper, we propose nonconvex stochastic Frank-Wolfe methods and analyze their convergence properties. Furthermore, for objective functions that decompose into a finite-sum, we leverage ideas from variance reduction for convex optimization to obtain new variance reduced nonconvex Frank-Wolfe methods that have provably faster convergence than the classical Frank-Wolfe method.

关键词： Methods Nonconvex Optimization

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Test-Time Certifiable Self-Supervision to Bridge the Sim2Real Gap in Event-Based Satellite Pose Estimation

Test-Time Certifiable Self-Supervision to Bridge the Sim2Rea...

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IEEE/RSJ International Conference on Intelligent Robots and systems (IROS)

作者： Mohsi Jawaid Rajat Talak Yasir Latif Luca Carlone Tat-Jun Chin Sentient Satellites Laboratory (SSL) Australian Institute for Machine Learning (AIML) The University of Adelaide SA Australia Laboratory for Information and Decision Systems (LIDS) Massachusetts Institute of Technology Cambridge MA USA

ISBN: (数字)9798350377705

ISBN: (纸本)9798350377712

Deep learning plays a critical role in vision-based satellite pose estimation. However, the scarcity of real data from the space environment means that deep models need to be trained using synthetic data, which raises the Sim2Real domain gap problem. A major cause of the Sim2Real gap are novel lighting conditions encountered during test time. Event sensors have been shown to provide some robustness against lighting variations in vision-based pose estimation. However, challenging lighting conditions due to strong directional light can still cause undesirable effects in the output of commercial off-the-shelf event sensors, such as noisy/spurious events and inhomogeneous event densities on the object. Such effects are non-trivial to simulate in software, thus leading to Sim2Real gap in the event domain. To close the Sim2Real gap in event-based satellite pose estimation, the paper proposes a test-time self-supervision scheme with a certifier module. Self-supervision is enabled by an optimisation routine that aligns a dense point cloud of the predicted satellite pose with the event data to attempt to rectify the inaccurately estimated pose. The certifier attempts to verify the corrected pose, and only certified test-time inputs are backpropagated via implicit differentiation to refine the predicted landmarks, thus improving the pose estimates and closing the Sim2Real gap. Results show that the our method outperforms established test-time adaptation schemes.

关键词： Training Satellites Event detection Pose estimation Pipelines Lighting Software Robustness Intelligent sensors Synthetic data

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Hierarchical Attention Network for Predicting DNA-Protein Binding Sites 15th

Hierarchical Attention Network for Predicting DNA-Protein Bi...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Yu, Wenbo Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

Discovering DNA-protein binding sites, also known as motif discovery, is the foundation for further analyses of transcription factors (TFs). Deep learning algorithms such as convolutional neural networks (CNN) and recurrent neural networks (RNN) are introduced to motif discovery task and have achieved state-of–art performance. However, these methods still have limitations such as neglecting the context information in large-scale sequencing data. Thus, inspired by the similarity between DNA sequence and human language, in this paper we propose a hierarchical attention network for predicting DNA-protein binding sites which is based on a natural language processing method for document classification. The proposed method is tested on real ChIP-seq datasets and the experimental results show a considerable improvement compared with two well-tested deep learning-based sequence model, DeepBind and Deepsea. © 2019, Springer Nature Switzerland AG.

关键词： Transcription factors

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Leaf Recognition Based on Capsule Network 15th

Leaf Recognition Based on Capsule Network

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Zheng, Yang Yuan, Chang-An Shang, Li Huang, Zhi-Kai Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China

ISBN: (纸本)9783030267629

Plant is an indispensable part of human life, so it is very important to identify and protect plants. Convolutional neural network is a commonly used neural network for image recognition. However, convolutional neural network has huge defects. In order to avoid the defects of convolutional neural network, this paper adopts a new type of neural network: capsule neural network to complete plant leaf recognition. The experiment shows that the capsule neural network has a more effective recognition rate than the common neural network in plant leaf recognition. © 2019, Springer Nature Switzerland AG.

关键词： Convolution

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Motif Discovery via Convolutional Networks with K-mer Embedding 15th

Motif Discovery via Convolutional Networks with K-mer Embedd...

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15th International Conference on Intelligent Computing, ICIC 2019

作者： Wang, Dailun Zhang, Qinhu Yuan, Chang-An Qin, Xiao Huang, Zhi-Kai Shang, Li Institute of Machine Learning and Systems Biology School of Electronics and Information Engineering Tongji University Shanghai China Science Computing and Intelligent Information Processing of GuangXi Higher Education Key Laboratory Nanning Normal University NanningGuangxi China College of Mechanical and Electrical Engineering Nanchang Institute of Technology NanchangJiangxi330099 China Department of Communication Technology College of Electronic Information Engineering Suzhou Vocational University SuzhouJiangsu215104 China

ISBN: (纸本)9783030269685

With the rapid development of deep learning, some discriminative motif discovery methods based on deep neural network are gradually becoming the mainstream, which also bringing huge improvement of prediction accuracy. In this paper, we propose a convolutional neural network based architecture (eCNN), combining embedding layer with GloVe. Firstly, eCNN divides each single sequence of ChIP-seq datasets into multiple subsequences called k-mers by a sliding window, and then encoding k-mers into a relatively low dimension vectors by GloVe, and finally scores each vector using multiple convolutional networks. The experiment shows that our architecture can get good results on the task of motif discovery. © 2019, Springer Nature Switzerland AG.

关键词： Embeddings

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