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The World Is Our Classroom: Developing a Model for international Virtual Internships - The Global Innovations Project 14

The World Is Our Classroom: Developing a Model for Internati...

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14th international conference on computer Supported Education (CSEDU)

作者： Doyle, Paul Keegan, Brian Gordon, Damian Becevel, Anna Gibson, Paul Jiang, Zhiying O'Sullivan, Dympna Technol Univ Dublin Sch Comp Sci Appl Social Comp Network Grp Dublin Ireland Telecom Sud Paris Dept Comp Sci Inst Mines Telecom Paris Paris France Beijing Univ Chem Technol Coll Informat Sci & Technol Beijing Peoples R China

ISBN: (纸本)9789897585623

In the aftermath of COVID-19, remote working has become the norm, and graduates now need an even wider range of skills, which traditional classrooms and internships do not always provide. Working in multiple time zones, within global multi-cultural teams, and only ever meeting colleagues through online technology are just some of the challenges, which require a new type of global graduate. Transversal skills including leadership, collaboration, innovation, digital, green, organization and communication skills are critical. The disruption from COVID-19 also presents unprecedented opportunities to develop more inclusive approaches to internships and international experiences, to level the playing field for students with special needs, from underrepresented groups or with caring commitments. In this position paper, we present a new Global Innovation internship model that has the aim of allowing students to complete technology internships and projects by working together virtually on real world challenges, guided by experienced industry and academic mentors. The model is being developed as part of an Erasmus+ funded project, and the partnership includes seven Higher Education Institutions from six different countries around the world. This position paper describes the design and development of a pilot programme of the Global Innovations internship model.

关键词： computer Science Education Virtual and Remote Internships

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Context Feature Guided network for Image Super-resolution 4

Context Feature Guided Network for Image Super-resolution

引用

4th international conference on Data Intelligence and Security, ICDIS 2022

作者： Ya, Mengxi Dai, Tao Liu, Ling Lv, Jieyin Xia, Shu-Tao Tsinghua Shenzhen International Graduate School Tsinghua University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China CIMC Intelligent Technology Co. Ltd. Shenzhen China

ISBN: (数字)9781665459686

ISBN: (纸本)9781665459686

Convolutional neural networks (CNNs) have achieved remarkable advance in single image super-resolution (SISR). While seeking for better performance, most existing SR methods prone to be deeper or wider structure are usually computationally intensive, which thus limits the application of SR networks in resource-limited devices. In this paper, a novel lightweight SR network named Context Feature Guided network (CFGN) is proposed for a better trade-off between performance and running efficiency. Specifically, to fully exploit semantic features, a novel Context Feature Guided Convolution (CFGC) is designed as the main building block for CFGN. The proposed CFGC consists of multiple stacked two-path fusion convolutions (TFCs), which could significantly enlarge the receptive field of each layer and learn more discriminative feature representations. To further consider rich multi-scale information, we design a context feature guided group (CFGG) as the main building block of CFGN by progressively stacking feature extraction distillation module and CFGCs. Extensive experiments demonstrate the superiority of the proposed method over the state-of-the-art lightweight SR models in terms of both quantitative metrics and visual quality. © 2022 IEEE.

关键词： Convolution

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A drawing retrieval model of mechanical parts based on YOLOv5 and a modified VGG network

A drawing retrieval model of mechanical parts based on YOLOv...

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2022 international conference on Frontiers of Communications, Information System and Data Science, CISDS 2022

作者： Huang, Yuming Ren, Bin Feng, Jihe Liu, Kaiwei Dongguan University of Technology School of Computer Science and Technology Dongguan China Dongguan University of Technology International School of Microelectronics Dongguan China

ISBN: (纸本)9781665464468

How to retrieve similar drawings correctly from the existing drawing database is of great significance for companies. In this paper, we propose a drawing retrieval model for mechanical parts based on YOLOv5 and a modified VGG network (FEN-VGG). In this paper, the function of YOLOv5 is to detect stereograms and views in drawings and to classify the targets within the drawings into view, part stereogram, title & material block, and revision block. Then, we use a feature extraction network with pre-trained VGG network, FEN-VGG, to extract the features of stereograms and views respectively. And get the vectors composed of stereograms features and views features. After that, the similarity is calculated by the inner product with the vectors in the stereogram database and the view database respectively. Finally, the top 5 drawings with the sum score are output. The experiments show that the Top-1 accuracy of our model reaches 90% and the Top-5 accuracy reaches 95%. Top-1 accuracy and Top-5 accuracy improved by 36% and 31% compared to the previous respectively. © 2022 IEEE.

关键词： Database systems

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Recent advances in deep learning based computer vision 3

Recent advances in deep learning based computer vision

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3rd international conference on computers, Information Processing and Advanced Education, CIPAE 2022

作者： Ma, Dongjian Leicester International Institute Dalian University of Technology Panjin China

ISBN: (纸本)9781665468121

Deep learning is a hot topic in current AI research and significant progress has been made in a variety of fields, including computer vision and natural language processing. Traditional architecture of deep learning normally based on convolutional neural networks. In recent years, impressive results from Transformer models on natural language tasks have intrigued the vision community to study their application to computer vision problems. Thus, neural networks with Transformer architecture have been introduced to computer vision tasks and even challenge mainstream of traditional convolutional neural network. In this paper, it will review recent improvements in computer vision based on deep learning method. It'll go over some of the most important research in deep learning models. After that, it focus on advances in four key computer vision tasks: picture classification, object identification, semantic segmentation, and human posture estimation. It'll go through the most recent developments in these fields. Finally, it will provide a conclution of recent computer vision advances and discuss future possibility. © 2022 IEEE.

关键词： Object detection

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Research on feature matching algorithm of remote sensing scenes 4

Research on feature matching algorithm of remote sensing sce...

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4th IEEE international conference on Civil Aviation Safety and Information technology, ICCASIT 2022

作者： Xu, Chenhang Dong, Enchong Zhang, Haiping Beijing University of Posts and Telecommunications International School Beijing China North China Electric Power University School of Control and Computer Engineering Beijing China East China Jiaotong University School of Information Engineering Nanchang China

ISBN: (数字)9781665467667

ISBN: (纸本)9781665467667

At present, satellite remote sensing technology has become a research hotspot for establishing space sensing systems at home and abroad. In remote sensing image processing, detecting and matching feature points in remote sensing images play a key role. In this article, three mainstream image feature extraction and matching methods are introduced, namely the SIFT method, SURF method, and ORB method. Experiments are designed to test and compare the operating efficiency and matching accuracy of these three feature point extraction and matching algorithms in remote sensing image scenes. The conclusion is as follows: ORB method is the fastest, SURF is second, and SIFT is the slowest. In terms of correct matching rate, the SURF algorithm and ORB algorithm have the advantage and show a high correct matching rate in remote sensing image scenes. © 2022 IEEE.

关键词： Remote sensing

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SIFT and RANSAC-Based Image Mosaic Algorithm for Image Compression

SIFT and RANSAC-Based Image Mosaic Algorithm for Image Compr...

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2022 international conference on Smart Applications, Communications and networking, SmartNets 2022

作者： Lungisani, Bose A. Lebekwe, Casper K. Zungeru, Adamu M. Botswana International University of Science and Technology Department of Electrical Computer and Telecommunications Engineering Palapye Botswana

ISBN: (纸本)9781665487580

Intensive research has been done on image mosaic algorithms to improve the field of view through generated image mosaics. The current imagery software packages used are computationally intensive to be used in real-time applications and are not economically affordable. Therefore, we have proposed an improved SIFT algorithm, which was then evaluated with an open-source algorithm and commercial software using structural similarity index measure (SSIM) and mosaicing computational times for mosaic accuracy and processing efficiency, respectively. Our approach demonstrated a significant improvement of more than 10% average on the mosaicing computational times for the five datasets used. Its mosaicing accuracy was found to be relatively within an acceptable range of above 90% averagely. © 2022 IEEE.

关键词： Image compression

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Image Translator: An Unsupervised Image-to-Image Translation Approach using GAN

Image Translator: An Unsupervised Image-to-Image Translation...

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2022 international conference on Innovations in Science, Engineering and technology, ICISET 2022

作者： Plabon, Silvia Satoar Khan, Mohammad Shabaj Khaliluzzaman, Md. Islam, Md. Rashedul International Islamic University Chittagong Dept. of Computer Science and Engineering Chattogram4318 Bangladesh

ISBN: (纸本)9781665483971

Unpaired image-to-image translation uses unpaired training data to predict domain-to-domain mapping. Unpaired Image-to-Image is a versatile Generative Adversarial network (GAN) model for image-to-image translation. Using the GAN architecture, you may train a generator model, which is often used to create graphics. To train the discriminator model, the generator is trained to fool the discriminator model into believing that the images are real. Generative adversarial networks (GAN) are a general-purpose solution for image-to-image translation challenges. These networks are concurrently learning a loss function that can be used to train the mapping they have learnt. This invention allows the use of the same fundamental strategy to solve problems that would otherwise need completely separate loss equations. We show that this approach may be used to synthesize photos from label maps, rebuild objects from edge maps, and colorize images. © 2022 IEEE.

关键词： Generative adversarial networks

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Performance Benchmarking of Visual Human Tracking algorithms for UAVs 26

Performance Benchmarking of Visual Human Tracking Algorithms...

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26th Pan-Hellenic conference on Informatics, PCI 2022

作者： Kalampokas, Theofanis Papakostas, George Chatzis, Vassilios Krinidis, Stelios MLV Research Group Department of Computer Science International Hellenic University Kavala Greece Department of Management Science and Technology International Hellenic University Kavala Greece

ISBN: (纸本)9781450398541

With the evolution of robotic systems, unmanned aerial vehicles (UAV) have become a target of interest for domains such as computer vision (CV) and artificial intelligence (AI), contributing to a variety of applications for surveillance, transportation and many more. A very hot topic that is the playground of the proposed benchmark is visual human tracking in images acquired by a camera mounted on a UAV. This target application troubles CV and deep learning (DL) research community in recent years and it has created serious demands for visual tracking algorithms. Some of the most important demands are high performance under hard visual tracking conditions and deployment in edge devices with limited computation resources. These two challenges are the main motivation of the presented paper, where 37 tracking algorithms have been benchmarked in visual object tracking (VOT) images. For each tracking algorithm two metric categories, relative to detection performance and hardware resources consumption, have been considered. The objective of the proposed paper is to highlight the most lightweight and high performance tracking algorithms for usage in UAV based applications. © 2022 Owner/Author.

关键词： computer vision

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Comparison between different frameworks in Visual Object Tracking 3

Comparison between different frameworks in Visual Object Tra...

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2022 3rd international conference on computer Information and Big Data Applications, CIBDA 2022

作者： Lu, Yifan Luo, Yayun International College Chongqing University of Posts and Telecommunications Chongqing China School of Automobile and Mechanical Engineering Changsha University of Science & Technology Changsha China

ISBN: (纸本)9783800758760

In recent years, deep learning-based visual tracking has flourished and a number of advances have been made. Numerous frameworks have been proposed to improve the accuracy and robustness. In this paper, we focus on introducing several different modified methods of visual tracking. We summarize the tracking framework as Siamese network, and correlation filter based methods. We review 10 distinguish methods published recently. We state that our review aims to provide an in-depth introduction on visual object tracking, which can guide the beginners to fast recognize this task. © VDE VERLAG GMBH - Berlin - Offenbach.

关键词： Deep learning

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Simultaneously Achieving Group Exposure Fairness and Within-Group Meritocracy in Stochastic Bandits 23

Simultaneously Achieving Group Exposure Fairness and Within-...

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23rd international conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Pokhriyal, Subham Jain, Shweta Ghalme, Ganesh Dhamal, Swapnil Gujar, Sujit Indian Institute of Technology Ropar Rupnagar India Indian Institute of Technology Hyderabad Hyderabad India International Institute of Information Technology Hyderabad Hyderabad India

Existing approaches to fairness in stochastic multi-armed bandits (MAB) primarily focus on exposure guarantee to individual arms. When arms are naturally grouped by certain attribute(s), we propose Bi-Level Fairness, which considers two levels of fairness. At the first level, Bi-Level Fairness guarantees a certain minimum exposure to each group. To address the unbalanced allocation of pulls to individual arms within a group, we consider meritocratic fairness at the second level, which ensures that each arm is pulled according to its merit within the group. Our work shows that we can adapt a UCB-based algorithm to achieve a Bi-Level Fairness by providing (i) anytime Group Exposure Fairness guarantees and (ii) ensuring individual-level Meritocratic Fairness within each group. We first show that one can decompose regret bounds into two components: (a) regret due to anytime group exposure fairness and (b) regret due to meritocratic fairness within each group. Our proposed algorithm BF-UCB balances these two regrets optimally to achieve the upper bound of O(√T) on regret;T being the stopping time. With the help of simulated experiments, we further show that BF-UCB achieves sub-linear regret;provides better group and individual exposure guarantees compared to existing algorithms;and does not result in a significant drop in reward with respect to UCB algorithm, which does not impose any fairness constraint. © 2024 international Foundation for Autonomous Agents and Multiagent Systems.

关键词： Stochastic systems

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