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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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Computational Tool to Support the Process of Teaching and Learning in the Discipline of Artificial Intelligence 17th

Computational Tool to Support the Process of Teaching and Le...

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17th Annual conference of the international-Association-of-Online-Engineering (IAOE) and the Global-Online-Laboratory-Consortium (GOLC) on Remote Engineering and Virtual Instrumentation (REV) - Cross Reality and Data Science in Engineering

作者： Roque Orfe, Bienvenido Hanley Milanes Luque, Maidelis Reyes Gonzalez, Yunia Martinez Sanchez, Natalia Luna Jimenez, Wilber Haro Perez, Madelin Univ Comp Sci Havana Cuba

ISBN: (纸本)9783030525750;9783030525743

the University of Computer Science is an educational-production centre that develops computer applications and services in different areas of knowledge. Because of the added value that the developed applications get when Artificial Intelligence techniques are used for decision making, the academic formation of the students in the different disciplines of the profession with emphasis on Artificial Intelligence is of high interest. this work describes a computational tool to support teaching that uses the conceptual algorithms of logical combinatorial recognition to determine the architecture and the set of initial weights of an artificial neural network, which contributes to the temporal efficiency of the learning process of the network and the efficiency of the classification. Experiments using Friedman's test and cross validation method demonstrate the applicability of this hybrid model in a Multilayer Perceptron.

关键词： Artificial intelligence Teaching-learning Artificial neural networks Conceptual algorithms Combinatorial logical pattern recognition

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2021 11th international conference on Information Science and Technology, ICIST 2021

2021 11th International Conference on Information Science an...

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11th international conference on Information Science and Technology, ICIST 2021

ISBN: (纸本)9781665412667

the proceedings contain 99 papers. the topics discussed include: command filter based event-triggered adaptive control for strict-feedback nonlinear systems with full state constraints;PointDet: An object detection framework based on human local features in the task of identifying violations;FPGA implementation of object detection accelerator based on Vitis-AI;digital twin enhanced assembly based on deep reinforcement learning;demand forecasting for shared umbrella using BP neural network;fully circuit implementation of a two-layer memristive neural network for pattern recognition;low-rank transfer learning for multi-stream data classification;using deep learning techniques to predict 10- year us treasury yield;and SegPoseNet: segmentation-guided 3D hand pose estimation.

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Ontological infrastructures for intelligent problem solving 10

Ontological infrastructures for intelligent problem solving

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Russian Advances in Fuzzy Systems and Soft computing: Selected Contributions to the 10th international conference on "Integrated Models and Soft computing in Artificial Intelligence", IMSC 2021

作者： Gribova, Valeriya V. Shalfeeva, Elena A. IACP FEB RAS Radio5 Vladivostok690041 Russia FEFU 10 Ajax Bay Russky Island Vladivostok690922 Russia

the main problem solved by the authors is to reduce the complexity of creating and maintaining systems with knowledge bases and increase their viability. the authors see its solution in the classification of intellectual tasks, which allows us to determine the place of the task to be solved among the many known tasks and ready-made solutions. Unified formal problem statements provide the basis for finding ready-made solvers and designing reusable system components. the ontological solver, together with the user interface, is repeatedly used for a variety of systems with knowledge bases, since it does not depend on the content of knowledge bases. So the quality of the created intelligent system can be infinitely improved by integrating more and more complete versions of knowledge bases with the solver. © 2021 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 international (CC BY 4.0).

关键词： Intelligent systems

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Review of Place recognition Approaches: Traditional and Deep Learning Methods 14th

Review of Place Recognition Approaches: Traditional and Deep...

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14th international conference on Applications of Fuzzy Systems, Soft computing, and Artificial Intelligence Tools, ICAFS 2020

作者： Taha, Mohammed Abdulghani Şah, Melike Direkoğlu, Cem Department of Computer Engineering Tishik International University Erbil Iraq Department of Computer Engineering Near East University via Mersin 10 Nicosia North Cyprus Turkey Department of Electrical and Electronics Engineering Middle East Technical University - Northern Cyprus Campus via Mersin 10 Kalkanli Guzelyurt North Cyprus Turkey

ISBN: (纸本)9783030640576

Place recognition is an important topic in many vision-based applications. For this purpose, many traditional and artificial intelligence-based methods have been proposed. Despite years of expertise accrued in this area, it still remains a challenging problem due to the various ways in which the appearance of places in the real world can take place. Recently, rapid improvements have been made in image processing and recognition using deep neural networks. In particular, convolutional neural frameworks (CNN) are widely used in object detection, image classification, as well as in place recognition. the advantage of CNN-based place recognition is that CNN methods can automatically learn image patterns using sample images without any pre-processing and can handle appearance variations better than traditional methods. In this paper, we review and discuss traditional and deep learning-based place recognition algorithms, as well as existing datasets that can be used for performance measurement. © 2021, the Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Object detection

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What's in a pattern: A Vocabulary to Articulate Group Routines and Practices 21

What's in a Pattern: A Vocabulary to Articulate Group Routin...

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10th international conference on Communities and Technologies: Wicked Problems in the Age of Tech, C and T 2021

作者： Bødker, Susanne Korsgaard, Henrik Lyle, Peter Aarhus University Department of Computer Science Denmark

ISBN: (纸本)9781450390569

When people come together as part of a community, oriented towards a collective activity, over an extended period of time, they develop and maintain different routines for the way they are organized, delegate, and carry out their activities. these routines involve a mixture of artifacts and technologies, and are shaped by key common dimensions such as how regulated an activity is, or whether it must follow a particular order. the routines also becomes inherited by new members of the community who use and develop them in their activity. We propose that similar routines, taking place across different communities, with their technologies and dimensions can be expressed and understood as a pattern. We present four patterns as examples that highlight the ways in which communities carry out routines related to organizing and engaging in their joint activity and discuss how such ways of addressing patterns may support the design of new community technologies. © 2021 ACM.

关键词： patterns collectives communities Routines

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Data Mining Technology Based on Granular computing in Computer Network Virus Defense 10th

Data Mining Technology Based on Granular Computing in Comput...

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10th international conference on Frontier computing, FC 2020

作者： Lan, Conghua Lanzhou Institute of Technology Lanzhou China

ISBN: (纸本)9789811601149

As is known to all, with the continuous development of computer technology, computer viruses are also increasingly prominent, especially the phenomenon of computer virus database is increasingly rampant, and the emergence of this phenomenon seriously affects the countries and the security of information technology industries and even affects people’s daily life and work which caused great threat. In order to solve this problem, this paper studies the application of data mining technology based on granular computing in computer network virus defense. Based on the characteristics of network virus and communication mode, data mining technology which is simply introduced, and the data mining technology in the concrete, this paper discusses the application of the computer network virus defense, granular computing is established under the basis of simulation model, the level of defense was tested, the result showed that the virus detection rate is higher than 97.2%, and this helps to ensure the security of computer network system. © 2021, the Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data mining

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An Improved Deterministic Stochastic MAC (SC-MAC) for High Power Efficiency Design 1

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29th IFIP WG 10.5/IEEE international conference on Very Large Scale Integration (VLSI-SoC)

作者： Wong, Ming Ming Chen, Lu Anh Tuan Do ASTAR Inst Microelect IME Singapore Singapore

ISBN: (数字)9783031168185

ISBN: (纸本)9783031168185;9783031168178

Convolutional Neural Network (CNN) is widely acknowledged as an effective machine learning model for various detection and recognition tasks. However, CNN often requires a significant amount of hardware resources and is high in its power consumption. this hinders the widespread deployment of CNN model in embedded systems and wearable devices. therefore, stochastic computing (SC) which leverages the power-accuracy trade-off, began to gain popularity in various neural network (NN) implementations. this paper presents an improved SC multiply-and-accumulate (MAC) unit that can be utilized as convolution engines in CNN. the proposed SC-MAC is operated using deterministic sequence and the design achieves latency and power reductions through parallelism and split mechanism optimizations. Furthermore, we also introduce decoder-based Stochastic Number Generator (SNG) that is capable of generating uncorrelated and segmented stochastic number (SN) without using random sources. the proposed deterministic and split SC-MAC is synthesized using typical libraries of UMC 40 nm technology for detailed hardware evaluation. the functionality of the presented SC-MAC is also verified in CNN using the MNIST dataset. Overall, our SC-MAC is proven to achieve higher power efficiency (GMACS/mW) and lower in energy consumption (pJ/MAC) as compared to the related works.

关键词： Stochastic computing (SC) Stochastic Number Generator (SNG) multiply-and-accumulate (MAC) Shared segmented/split design Convolution engine

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Image Classification Using Convolutional Neural Networks 4th

Image Classification Using Convolutional Neural Networks

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4th international conference on Advanced Informatics for computing Research, ICAICR 2020

作者： Handa, Nishit Kaushik, Yash Sharma, Nikhil Dixit, Muskaan Garg, Monika Manav Rachna International Institute of Research and Studies Faridabad India

ISBN: (纸本)9789811636592

the term Deep Learning can be termed as the subset of artificial intelligence with multiple network layers forming neural patterns. People’s interest in having the knowledge of deep hidden layers have recently boosted and have begun to takeover various classical strategical performances in numerous fields;especially in pattern recognition and image recognition. One of the most talked-about and important part in deep learning and neural networks is about the Convolutional Neural Network (CNN). We have briefly described about how CNNs plays an integral role in the field of image recognition. Starting from the concepts of Deep Learning and Neural Networks we make our way for *** have given the idea of how CNN works along with convolutional layers and convolutional filters in a lay-man language and the simplest way possible. Further, how a convolutional filter classifies objects and shapes is also explained in the paper. Later in the paper we have also discussed the advantages of CNN over any other neural network technique. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image classification

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Relevance of Near-Term Quantum computing in the Cloud: A Humanities Perspective 1

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10th international conference on Cloud computing and Services Science (CLOSER)

作者： Barzen, Johanna Leymann, Frank Falkenthal, Michael Vietz, Daniel Weder, Benjamin Wild, Karoline Univ Stuttgart Univ Str 38 D-70569 Stuttgart Germany StoneOne AG Keithstr 6 D-10787 Berlin Germany

ISBN: (数字)9783030723699

ISBN: (纸本)9783030723682;9783030723699

As quantum computers are becoming real, they have the inherent potential to significantly impact many application domains. In this paper we outline the fundamentals about programming quantum computers and show that quantum programs are typically hybrid consisting of a mixture of classical parts and quantum parts. With the advent of quantum computers in the cloud, the cloud is a fine environment for performing quantum programs. the tool chain available for creating and running such programs is sketched. As an exemplary problem we discuss efforts to implement quantum programs that are hardware independent. A use case from quantum humanities is discussed, hinting which applications in this domain can already be used in the field of (quantum) machine learning. Finally, a collaborative platform for solving problems with quantum computers - that is currently under construction - is presented.

关键词： Cloud computing Quantum computing Hybrid applications Quantum humanities

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10th international conference on Numerical Geometry, Grid Generation, and Scientific computing celebrating the 130th anniversary of B. N. Delaunay, NUMGRID 2020

10th International Conference on Numerical Geometry, Grid Ge...

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10th international conference on Numerical Geometry, Grid Generation, and Scientific computing celebrating the 130th anniversary of B. N. Delaunay, NUMGRID 2020

ISBN: (纸本)9783030767976

the proceedings contain 25 papers. the special focus in this conference is on Numerical Geometry, Grid Generation, and Scientific computing. the topics include: Efficient Steady Flow Computations with Exponential Multigrid Methods;exponential Time Integrators for Unsteady Advection–Diffusion Problems on Refined Meshes;a Proof of the Invariant-Based Formula for the Linking Number and Its Asymptotic Behaviour;the Singularity Set of Optimal Transportation Maps;polygonal and Polyhedral Delaunay Meshing;on Decomposition of Embedded Prismatoids in 3 Without Additional Points;out-of-core Constrained Delaunay Tetrahedralizations for Large Scenes;size Gradation Control for Anisotropic Hybrid Meshes;Adjoint Computation on Anisotropic Meshes in High-fidelity RANS Simulations;preface;foreword;local Groups in Delone Sets;moving Deforming Mesh Generation Based on the Quasi-Isometric Functional;Adaptive Grids for Non-monotone Waves and Instabilities in a Non-equilibrium PDE Model;RBF-VerBSS Hybrid Method for Mesh Deformation;a Uniform Convergence Analysis for a Bakhvalov-Type Mesh with an Explicitly Defined Transition Point;on a Comprehensive Grid for Solving Problems Having Exponential or Power-of-First-Type Layers;preserved Structure Constants for Red Refinements of Product Elements;global Parametrization Based on Ginzburg-Landau Functional;parametrization of Plane Irregular Regions: A Semi-automatic Approach I;a Hybrid Approach to Fast Indirect Quadrilateral Mesh Generation;hexahedral Mesh Generation Using Voxel Field Recovery;manifolds of Triangulations, Braid Groups of Manifolds, and the Groups Γnk;generation of Boundary Layer Meshes by the Enhanced Jump-and-Walk Method with a Fast Collision Detecting Algorithm.

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