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5th International Conference on High Performance Big data and Intelligent Systems, HDIS 2023

作者： Zhou, Qi Yang, Kai Ke, Zunwang Wang, Gang Zhang, Yugui Jia, Yizhen Cao, Fengcai Ma, Junxiao Liu, Changlin Zhang, Kaijie Wu, Min School of Semiconductor Science and Technology South China Normal University GuangZhou510631 China CGNPC Uranium Industry Development Co. Ltd. Beijing100020 China School of Software Xinjiang University Urumqi830046 China School of Computing and Data Engineering NingboTech University Ningbo315100 China AnnLab Institute of Semiconductors Chinese Academy of Sciences Beijing100083 China Beijing Key Laboratory of Semiconductor Neural Network Intelligent Sensing and Computing Technology Beijing100083 China School of Integrated Circuits University of Chinese Academy of Sciences Beijing100049 China Co. Ltd Beijing100176 China School of Information Beijing Forestry University Beijing100083 China

ISBN: (纸本)9798350394160

In real-world scenarios, capturing scenes with excessive dynamic range often leads to the partial loss of highlight or dark area information due to irradiance variations and limitations in the capture capabilities of imaging devices caused by their size and material constraints. This phenomenon gives rise to anomalous exposure problems and triggers the loss of crucial initial details, thereby becoming a significant obstacle to achieving high image quality. Despite numerous researchers’ integration of exposure compensation techniques into the imaging process, the anomalous exposure problem remains unsolved, hindering progress in image processing. Consequently, considerable research is valuable in implementing exposure calibration techniques on visual perception and multimedia devices. This dissertation proposes FARDBUNet, a novel end-to-end exposure calibration model designed to address the anomalous exposure problem. A key innovation of FARDBUNet is the incorporation of a fused attention mechanism, enabling the model to prioritize regions needing enhanced correction while accounting for the global exposure characteristics of the image. Additionally, to tackle the challenge of inadequate information availability in the localized areas due to exposure issues, we introduce an atrous residual dense block that efficiently captures local information by expanding the receptive field. Comprehensive tests validate the effectiveness of our suggested model, which performs on par with leading techniques on expansive exposure datasets. ©2023 IEEE.

关键词： Image enhancement

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A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Model for Classifying DNA Barcodes

A Variational Autoencoder-Feed Forward Networks (VAE-FNN) Mo...

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Advances in Electrical, Electronics and Computational Intelligence (ICAEECI), International Conference on

作者： Abhivyakti Yadav Divya S I R Oviya Kalpana Raja Department of Computer Science and Engineering (Artificial Intelligence) Amrita School of Computing Amrita Vishwa Vidyapetham Chennai India Section for Biomedical Informatics and Data Science School of Medicine Yale University New Haven Connecticut USA

DNA Barcodes, which are particular fragments derived from brief sections of DNA (such as mitochondrial, nuclear, and plastid sequences), can be used to identify organisms from the major life kingdoms. In addition to supporting conventional taxonomic techniques, DNA barcoding is a potent tool that advances our knowledge of species diversity and their ecological functions. On a variety of organisms, the use of this approach for species categorization has been successful. In this paper, we examine how DNA barcoding has been used to classify species based on DNA barcodes as well as other related research that has been done over the years on the subject. After experimenting with a number of deep learning models, we have propose a Variational Auto Encoder + Feed Forward Neural Network workflow for classifiying species using DNA barcodes. The models have been assessed on the basis of performance factors including accuracy, recall, and precision. COI, rbcL, matK, and ITS are the specific gene sections that have been identified as barcodes. For both simulated and real datasets, the model can attain an average accuracy of greater than 95 percent. This DNA barcoding approach has the ability to simplify DNA barcode-based species identification and serve as a tool for species categorization.

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Impact of Fuzziness for Skin Lesion Classification with Transformer-Based Model

Impact of Fuzziness for Skin Lesion Classification with Tran...

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International Conference on computing, Electronics & Communications engineering (iCCECE)

作者： Israt Yasmin Suriya Sultana Syeda Jobaida Begum Muhammed J. A. Patwary Tarik Adnan Almohamad Iftekhar Salam Department of Computer Science and Engineering International Islamic University Chittagong Chattogram Bangladesh Electrical-Electronics Engineering Karabuk University Karabuk Türkiye School of Computing and Data Science Xiamen University Malaysia Sepang Malaysia

Skin lesion is one of the most commonly encountered illnesses that need to be detected and treated at an early stage. Numerous Convolutional Neural Network (CNN) classifiers were utilized to identify skin conditions using visual features. When it comes to computational effectiveness and accuracy, the recently released Vision Transformers (ViT) have been shown to outperform CNNs. Since the ViTs perform well in classifying images, we use ViT models in our approach. This research proposes a fuzziness-based transformer model to classify images. The divide-and-conquer technique is employed to examine the effectiveness of fuzzy logic and classifying images more accurately. To increase the effectiveness of image classification, the testing dataset has been divided into three groups, including low, medium, and high-fuzzy instances, based on the degree of fuzziness for each sample. For better classification of images, we propose a straightforward mechanism that consists of a fuzzy divide-and-conquer strategy with ViT. Experiments are carried out to compare the model’s performance with and without using this proposed mechanism. We noticed that retraining classifiers with low fuzzy samples can enhance image classifier performance. The classification performance on the PAD-UFES-20 dataset using this strategy revealed substantial improvements in the classifier’s performance.

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SAFL-Net: Semantic-Agnostic Feature Learning Network with Auxiliary Plugins for Image Manipulation Detection

SAFL-Net: Semantic-Agnostic Feature Learning Network with Au...

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International Conference on Computer Vision (ICCV)

作者： Zhihao Sun Haoran Jiang Danding Wang Xirong Li Juan Cao Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences School of Mathematics Science University of Chinese Academy of Sciences MoE Key Lab of Data Engineering and Knowledge Engineering Renmin University of China

Since image editing methods in real world scenarios cannot be exhausted, generalization is a core challenge for image manipulation detection, which could be severely weakened by semantically related features. In this paper we propose SAFL-Net, which constrains a feature extractor to learn semantic-agnostic features by designing specific modules with corresponding auxiliary tasks. Applying constraints directly to the features extracted by the encoder helps it learn semantic-agnostic manipulation trace features, which prevents the biases related to semantic information within the limited training data and improves generalization capabilities. The consistency of auxiliary boundary prediction task and original region prediction task is guaranteed by a feature transformation structure. Experiments on various public datasets and comparisons in multiple dimensions demonstrate that SAFL-Net is effective for image manipulation detection.

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A discovery of two slow pulsars with FAST:“Ronin”from the globular cluster M15

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Science China(Physics,Mechanics & Astronomy) 2024年第6期67卷 166-180页

作者： Dengke Zhou Pei Wang Di Li Jianhua Fang Chenchen Miao Paulo CCFreire Lei Zhang Dandan Zhang Huaxi Chen Yi Feng Yifan Xiao Jintao Xie Xu Zhang Chenwu Jin Han Wang Yinan Ke Xuerong Guo Rushuang Zhao Chenhui Niu Weiwei Zhu Mengyao Xue Yabiao Wang Jiafu Wu Zhenye Gan Zhongyi Sun Chengjie Wang Jie Zhang Junshuo Zhang Jinhuang Cao Wanjin Lu Research Center for Astronomical Computing Zhejiang LaboratoryHangzhou 311121China National Astronomical Observatories Chinese Academy of SciencesBeijing 100101China Institute for Frontiers in Astronomy and Astrophysics Beijing Normal UniversityBeijing 102206China University of Chinese Academy of Sciences Beijing 100049China New Cornerstone Science Laboratory Shenzhen 518054China Max Planck Institut f¨ur Radioastronomie Bonn 53121Germany Centre for Astrophysics and Supercomputing Swinburne University of TechnologyHawthorn 3122Australia School of Mathematical Sciences School of Physics and Electronic SciencesGuizhou Normal UniversityGuiyang 550001China Guizhou Provincial Key Laboratory of Radio Astronomy and Data Processing Guiyang 550001China School of Physics and Electronic Engineering Qilu Normal UniversityJinan 250200China School of Physics and Electronic Science Guizhou Normal UniversityGuiyang 550001China College of Physical Science and Technology Central China Normal UniversityWuhan 430079China Tencent Youtu Lab Shanghai 201103China

Globular clusters harbor numerous millisecond pulsars,but long-period pulsars(P 100 ms)are rarely *** this study,we employed a fast folding algorithm to analyze observational data from multiple globular clusters obtained by the Five-hundredmeter Aperture Spherical radio Telescope(FAST),aiming to detect the existence of long-period *** estimated the impact of the median filtering algorithm in eliminating red noise on the minimum detectable flux density(S_(min))of ***,we successfully discovered two isolated long-period pulsars in M15 with periods approximately equal to 1.928451 and3.960716 s,*** the P-˙P diagram,both pulsars are positioned below the spin-up line,suggesting a possible history of partial recycling in X-ray binary systems disrupted by dynamical encounters later *** to timing results,these two pulsars exhibit remarkably strong magnetic *** the magnetic fields were weakened during the accretion process,then a short duration of accretion might explain the strong magnetic fields of these pulsars.

关键词： pulsar globular cluster FAST

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Immersive Biography: Supporting Intercultural Empathy and Understanding for Displaced Cultural Objects in Virtual Reality 25

Immersive Biography: Supporting Intercultural Empathy and Un...

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2025 CHI Conference on Human Factors in computing Systems, CHI 2025

作者： Zhao, Ke Chen, Ruiqi Zhang, Xiaziyu Wang, Chenxi Chen, Siling Wang, Xiaoguang Wang, Yujue Tong, Xin School of Information Management Wuhan University Wuhan China Duke Kunshan University Kunshan China Human Centered Design & Engineering University of Washington SeattleWA United States School of Design Politecnico di Milano Milan Italy Intelligent Computing Laboratory for Cultural Heritage Wuhan University Wuhan China Guangzhou China Data Science Research Center Duke Kunshan University Suzhou Kunshan China

ISBN: (纸本)9798400713941

Displaced cultural objects often act as mediators of intercultural understanding due to their connection between the original and host communities. This study explores how immersive embodied VR biography enhances intercultural empathy and understanding of displaced cultural objects. We took the famous Chinese painting, the Admonitions Scroll, housed at the British Museum as an example to design an Immersive Biography in VR. We conducted an empirical study with 24 participants from source and non-source communities. Findings suggested that interacting with biographical narratives of displaced cultural objects in a personified embodied way can effectively promote intercultural empathy and understanding. Additionally, simulated intercultural scenarios and dialogues with personified cultural objects fostered intercultural empathy in both groups, with a stronger effect observed in non-source communities due to differences in cultural identity and personal connections. Our study provided the potential and practical insights of immersive technologies to inspire intercultural communication for displaced cultural objects. © 2025 Copyright held by the owner/author(s). Publication rights licensed to ACM.

关键词： Biographies

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SWIPT-Enabled MISO Ad Hoc Network Underlay RSMA-based Cellular Network with IRS

SWIPT-Enabled MISO Ad Hoc Network Underlay RSMA-based Cellul...

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IEEE Conference on Vehicular Technology (VTC)

作者： Nguyen Thi Thanh Van Nguyen Cong Luong Feng Shaohan Shimin Gong Dusit Niyato Dong In Kim Faculty of Computer Science PHENIKAA University Hanoi Vietnam School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China School of Intelligent Systems Engineering Sun Yat-sen University Guangzhou China College of Computing and Data Sciene Nanyang Technological University Singapore Department of Electrical and Computer Engineering Sungkyunkwan University Suwon South Korea

ISBN: (数字)9798350387414

ISBN: (纸本)9798350387421

In this paper, we propose a simultaneous wire-less information and power transfer (SWIPT)-enabled Ad hoc network underlay rate-splitting multiple access (RSMA)-based system with intelligent reflecting surface (IRS). Therein, a base station (BS) in a primary network uses RSMA to serve primary users (PUs), and secondary user (SU) pairs constitute an Ad hoc network sharing the spectrum with the primary network. The power splitting (PS)-based SWIPT protocol is used in the Ad hoc network that allow the SU receivers to decode the information and harvest energy simultaneously. An IRS is deployed to further enhance the system performance. We formulate optimization problems that optimize the common data rate allocation and beamformers associated with the common and private messages of RSMA, the beamformers and the PS factor in the Ad hoc network, and reflection coefficients of the IRS to maximize the minimum rate of the PUs while satisfying the requirements of harvested energy and data rate of the SU pairs. The optimization problems are non-convex and challenging to be solved. We propose a low complexity algorithm based on alternating descent techniques. Numerical results demonstrate the effectiveness and improvement of the proposed framework compared with the framework based on existing multiple access schemes, i.e., non-orthogonal multiple access (NOMA).

关键词： Cellular networks NOMA Vehicular and wireless technologies System performance Simulation Throughput Ad hoc networks

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Optimal Control Operator Perspective and a Neural Adaptive Spectral Method

arXiv

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arXiv 2024年

作者： Feng, Mingquan Chen, Zhijie Huang, Yixin Liu, Yizhou Yan, Junchi School of Electronic Information & Electrical Engineering Shanghai Jiao Tong University Shanghai China Siebel School of Computing and Data Science University of Illinois at Urbana-Champaign UrbanaIL United States School of Artificial Intelligence MoE Lab of AI Shanghai Jiao Tong University Shanghai China

Optimal control problems (OCPs) involve finding a control function for a dynamical system such that a cost functional is optimized. It is central to physical systems in both academia and industry. In this paper, we propose a novel instance-solution control operator perspective, which solves OCPs in a one-shot manner without direct dependence on the explicit expression of dynamics or iterative optimization processes. The control operator is implemented by a new neural operator architecture named Neural Adaptive Spectral Method (NASM), a generalization of classical spectral methods. We theoretically validate the perspective and architecture by presenting the approximation error bounds of NASM for the control operator. Experiments on synthetic environments and a real-world dataset verify the effectiveness and efficiency of our approach, including substantial speedup in running time, and high-quality in- and out-of-distribution generalization. © 2024, CC BY-NC-ND.

关键词： Optimal control systems

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Q&A: Query-Based Representation Learning for Multi-Track Symbolic Music re-Arrangement

arXiv

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arXiv 2023年

作者： Zhao, Jingwei Xia, Gus Wang, Ye School of Computing NUS Singapore Institute of Data Science NUS Singapore Integrative Sciences and Engineering Programme NUS Graduate School Singapore Music X Lab NYU Shanghai China MBZUAI United Arab Emirates

Music rearrangement is a common music practice of reconstructing and reconceptualizing a piece using new composition or instrumentation styles, which is also an important task of automatic music generation. Existing studies typically model the mapping from a source piece to a target piece via supervised learning. In this paper, we tackle rearrangement problems via self-supervised learning, in which the mapping styles can be regarded as conditions and controlled in a flexible way. Specifically, we are inspired by the representation disentanglement idea and propose Q&A, a query-based algorithm for multi-track music rearrangement under an encoder-decoder framework. Q&A learns both a content representation from the mixture and function (style) representations from each individual track, while the latter queries the former in order to rearrange a new piece. Our current model focuses on popular music and provides a controllable pathway to four scenarios: 1) re-instrumentation, 2) piano cover generation, 3) orchestration, and 4) voice separation. Experiments show that our query system achieves high-quality rearrangement results with delicate multi-track structures, significantly outperforming the baselines. © 2023, CC BY.

关键词： Music

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Provably secure fog-based authentication protocol for VANETs

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Computer Networks 2024年 246卷

作者： Awais, Syed Muhammad Yucheng, Wu Mahmood, Khalid Badar, Hafiz Muhammad Sanaullah Kharel, Rupak Das, Ashok Kumar School of Microelectronics and Communication Engineering Chongqing University Chongqing China Graduate School of Intelligent Data Science National Yunlin University of Science and Technology Yunlin Douliu64002 Taiwan Department of Emerging Computing Technologies Emerson University Multan Pakistan School of Computing and Engineering University of Huddersfield HuddersfieldHD1 3DH United Kingdom The Faculty of Electrical and Electronics Engineering Ton Duc Thang University Ho Chi Minh City Viet Nam Center for Security Theory and Algorithmic Research International Institute of Information Technology Hyderabad500 032 India

The integration of fog computing and vehicular ad-hoc networks (VANETs) has become increasingly important due to advancements in cloud computing, Internet of Things (IoT) technologies, and intelligent transportation systems. However, ensuring secure communication in fog-based VANETs poses a significant challenge. To address this challenge, we propose a novel authenticated key agreement protocol that achieves mutual authentication, generates a secure session key for confidential communication, and ensures privacy protection without relying on bilinear pairing. We provide rigorous security proofs for our protocol, specifically tailored for fog-based VANETs, demonstrating its ability to meet their stringent security requirements. Simulations using NS3 allow for a comprehensive performance evaluation, providing insights into the protocol's efficiency and scalability. Additionally, our protocol is highly efficient, featuring low computation and communication costs, thereby offering a practical and scalable solution for secure communication in fog-based VANETs. © 2024

关键词： Fog

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