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arXiv 2024年

作者： Wu, Xu Hou, XianXu Lai, Zhihui Zhou, Jie Zhang, Ya-Nan Pedrycz, Witold Shen, Linlin The Computer Vision Institute College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Shenzhen Institute of Artificial Intelligence and Robotics for Society Shenzhen518060 China Guangdong Key Laboratory of Intelligent Information Processing Shenzhen University Shenzhen518060 China School of AI and Advanced Computing Xi’an Jiaotong-Liverpool University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University SZU Branch Shenzhen Institute of Artificial Intelligence and Robotics for Society Guangdong Shenzhen518060 China The Department of Electrical & Computer Engineering University of Alberta University of Alberta Canada

Low-light image enhancement (LLIE) aims to improve low-illumination images. However, existing methods face two challenges: (1) uncertainty in restoration from diverse brightness degradations;(2) loss of texture and color information caused by noise suppression and light enhancement. In this paper, we propose a novel enhancement approach, CodeEnhance, by leveraging quantized priors and image refinement to address these challenges. In particular, we reframe LLIE as learning an image-to-code mapping from low-light images to discrete codebook, which has been learned from high-quality images. To enhance this process, a Semantic Embedding Module (SEM) is introduced to integrate semantic information with low-level features, and a Codebook Shift (CS) mechanism, designed to adapt the pre-learned codebook to better suit the distinct characteristics of our low-light dataset. Additionally, we present an Interactive Feature Transformation (IFT) module to refine texture and color information during image reconstruction, allowing for interactive enhancement based on user preferences. Extensive experiments on both real-world and synthetic benchmarks demonstrate that the incorporation of prior knowledge and controllable information transfer significantly enhances LLIE performance in terms of quality and fidelity. The proposed CodeEnhance exhibits superior robustness to various degradations, including uneven illumination, noise, and color distortion. Copyright © 2024, The Authors. All rights reserved.

关键词： Image enhancement

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Modeling Multiple Views via Implicitly Preserving Global Consistency and Local Complementarity

arXiv

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arXiv 2022年

作者： Li, Jiangmeng Qiang, Wenwen Zheng, Changwen Su, Bing Razzak, Farid Wen, Ji-Rong Xiong, Hui The University of Chinese Academy of Sciences Beijing China The Science & Technology on Integrated Information System Laboratory Institute of Software Chinese Academy of Sciences Beijing China The New York University Columbia University New York United States The Beijing Key Laboratory of Big Data Management and Analysis Methods Gaoling School of Artificial Intelligence Renmin University of China Beijing100872 China Guangzhou China Department of Computer Science & Engineering The Hong Kong University of Science and Technology Hong Kong

While self-supervised learning techniques are often used to mine hidden knowledge from unlabeled data via modeling multiple views, it is unclear how to perform effective representation learning in a complex and inconsistent context. To this end, we propose a new multi-view self-supervised learning method, namely consistency and complementarity network (CoCoNet), to comprehensively learn global inter-view consistent and local cross-view complementarity-preserving representations from multiple views. To capture crucial common knowledge which is implicitly shared among views, CoCoNet employs a global consistency module that aligns the probabilistic distribution of views by utilizing an efficient discrepancy metric based on the generalized sliced Wasserstein distance. To incorporate cross-view complementary information, CoCoNet proposes a heuristic complementarity-aware contrastive learning approach, which extracts a complementarity-factor jointing cross-view discriminative knowledge and uses it as the contrast to guide the learning of view-specific encoders. Theoretically, the superiority of CoCoNet is verified by our information-theoretical-based analyses. Empirically, our thorough experimental results show that CoCoNet outperforms the state-of-the-art self-supervised methods by a significant margin, for instance, CoCoNet beats the best benchmark method by an average margin of 1.1% on ImageNet. Copyright © 2022, The Authors. All rights reserved.

关键词： Supervised learning

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Identifying RNA-binding proteins using multi-label deep learning

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Science China(information Sciences) 2019年第1期62卷 217-219页

作者： Xiaoyong PAN Yong-Xian FAN Jue JIA Hong-Bin SHEN Department of Medical Informatics Erasmus MC Guangxi Key Laboratory of Trusted Software Guilin University of Electronic Technology Affiliated Hospital of Jiangsu University Department of Automation Shanghai Jiao Tong University Key Laboratory of System Control and Information Processing Ministry of Education of China

Dear editor,RNA-binding proteins (RBPs) are involved in both transcriptional and post-transcriptional gene regulation, such as RNA splicing and localization. In addition, their dysregulations are closely associated with many diseases [1]. For example, mutations in the RBPs FUS and TDP-43that can cause amyotrophic lateral sclerosis [2].

关键词： RBP RNA Identifying RNA-binding proteins using multi-label deep learning

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Utilizing Multi-Agent systems in Grid Environments 6

Utilizing Multi-Agent Systems in Grid Environments

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6th HCT information technology Trends, ITT 2019

作者： Alwada'n, Tariq Alarabeyyat, Abdulsalam Khdour, Thair Rodan, Ali Higher Colleges of Technology Computer Information System Department Al Ain United Arab Emirates Al-Balqa' Applied University Software Engineering Department Salt Jordan

ISBN: (纸本)9781728150611

Grid Computing refers to systems and applications that incorporate and control distributed services and resources to resolve systematic or industrial issues. Over the past few years, developers have realized the need for an automatic mechanism that can be used in employing the grid power, improving its operating and enhancing its production. Multi-Agents systems are generally employed to solve problems by using decentralized techniques by a set of agents collaborate to resolve a challenge. Hence, it is considered suitable solutions for open systems that change frequently. In this paper, we propose a modern architecture that utilizes a Multi Agent system not only to help in choosing the best resources but also to generate the negotiation protocol between the grid users and providers to fully deploy the capacity of grid computing. Moreover, the proposed architecture can play the main role in monitoring the list of users' jobs as they are being handled. © 2019 IEEE.

关键词： Multi agent systems

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Loophole-free test of macroscopic realism via high-order correlations of measurement

arXiv

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arXiv 2024年

作者： Wang, Ping Chen, Chong Liao, Hao Vorobyov, Vadim V. Wrachtrup, Jörg Liu, Ren-Bao College of Education for the Future Beijing Normal University Zhuhai519087 China Department of Physics The Chinese University of Hong Kong New Territories Shatin Hong Kong Centre for Quantum Coherence The Chinese University of Hong Kong New Territories Shatin Hong Kong The Hong Kong Institute of Quantum Information Science and Technology The Chinese University of Hong Kong New Territories Shatin Hong Kong National Engineering Laboratory on Big Data System Computing Technology Guangdong Province Engineering Center of China-made High Performance Data Computing System College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China 3rd Institute of Physics Research Center SCoPE and IQST University of Stuttgart Stuttgart70569 Germany New Cornerstone Science Laboratory The Chinese University of Hong Kong New Territories Shatin Hong Kong

Test of macroscopic realism (MR) is key to understanding the foundation of quantum mechanics. Due to the existence of the non-invasive measurability loophole and other interpretation loopholes, however, such test remains an open question. Here we propose a general inequality based on high-order correlations of measurements for a loophole-free test of MR at the weak signal limit. Importantly, the inequality is established using the statistics of raw data recorded by classical devices, without requiring a specific model for the measurement process, so its violation would falsify MR without the interpretation loophole. The non-invasive measurability loophole is also closed, since the weak signal limit can be verified solely by measurement data (using the relative scaling behaviors of different orders of correlations). We demonstrate that the inequality can be broken by a quantum spin model. The inequality proposed here provides an unambiguous test of the MR principle and is also useful to characterizing quantum coherence. Copyright © 2024, The Authors. All rights reserved.

关键词： Quantum theory

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Reinforced MOOCs Concept Recommendation in Heterogeneous information Networks

arXiv

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arXiv 2022年

作者： Gong, Jibing Wan, Yao Liu, Ye Li, Xuewen Zhao, Yi Wang, Cheng Lin, Yuting Fang, Xiaohan Feng, Wenzheng Zhang, Jingyi Tang, Jie School of Information Science and Engineering The Key Lab for Computer Virtual Technology and System Integration The Key Laboratory for Software Engineering of Hebei Province Yanshan University No. 438 West Hebei Avenue Hebei Qinhuangdao066004 China School of Computer Science and Technology Huazhong University of Science and Technology Luoyu Road 1037 Hubei Wuhan430074 China Salesforce Research 556 Xixi Road Chicago United States School of Cyber Science and Technology Beihang University 37 Xueyuan Road Beijing100191 China Department of Computer Science Tsinghua University Beijing100084 China

Massive open online courses (MOOCs), which offer open access and widespread interactive participation through the internet, are quickly becoming the preferred method for online and remote learning. Several MOOC platforms offer the service of course recommendation to users, to improve the learning experience of users. Despite the usefulness of this service, we consider that recommending courses to users directly may neglect their varying degrees of expertise. To mitigate this gap, we examine an interesting problem © 2022, CC BY.

关键词： information services

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A novel Skin lesion prediction and classification technique: ViT-GradCAM

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Skin research and technology : official journal of International Society for Bioengineering and the Skin (ISBS) [and] International Society for Digital Imaging of Skin (ISDIS) [and] International Society for Skin Imaging (ISSI) 2024年第9期30卷 e70040页

作者： Muhammad Shafiq Kapil Aggarwal Jagannathan Jayachandran Gayathri Srinivasan Rajasekhar Boddu Adugna Alemayehu School of Information Engineering Qujing Normal University Qujing China. Key Laboratory of Intelligent Sensor and System Design College of Information Engineering Qujing Normal University Qujing China. Department of CSE Koneru Lakshmaiah Education Foundation Vaddeswaram Andhra Pradesh India. Department of Software and Systems Engineering School of Computer Science Engineering and Information Systems Vellore Institute of Technology Katpadi Vellore India. Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India. Department of AIML Gokaraju Rangaraju Institute of Engineering and Technology Bachupally Hyderabad India. Lecturer in Software Engineering Wachemo University Hosaina Ethiopia.

BACKGROUND:Skin cancer is one of the highly occurring diseases in human life. Early detection and treatment are the prime and necessary points to reduce the malignancy of infections. Deep learning techniques are supplementary tools to assist clinical experts in detecting and localizing skin lesions. Vision transformers (ViT) based on image segmentation classification using multiple classes provide fairly accurate detection and are gaining more popularity due to legitimate multiclass prediction capabilities. MATERIALS AND METHODS:In this research, we propose a new ViT Gradient-Weighted Class Activation Mapping (GradCAM) based architecture named ViT-GradCAM for detecting and classifying skin lesions by spreading ratio on the lesion's surface area. The proposed system is trained and validated using a HAM 10000 dataset by studying seven skin lesions. The database comprises 10 015 dermatoscopic images of varied sizes. The data preprocessing and data augmentation techniques are applied to overcome the class imbalance issues and improve the model's performance. RESULT:The proposed algorithm is based on ViT models that classify the dermatoscopic images into seven classes with an accuracy of 97.28%, precision of 98.51, recall of 95.2%, and an F1 score of 94.6, respectively. The proposed ViT-GradCAM obtains better and more accurate detection and classification than other state-of-the-art deep learning-based skin lesion detection models. The architecture of ViT-GradCAM is extensively visualized to highlight the actual pixels in essential regions associated with skin-specific pathologies. CONCLUSION:This research proposes an alternate solution to overcome the challenges of detecting and classifying skin lesions using ViTs and GradCAM, which play a significant role in detecting and classifying skin lesions accurately rather than relying solely on deep learning models.

关键词： Grad‐CAM Multi‐Headed Self attention (MSA) linear patch projection multilayer perceptron (MLP) skin lesion transformer encoder vision transformer (ViT)

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Unleashing the Power of Edge-Cloud Generative AI in Mobile Networks: A Survey of AIGC Services

arXiv

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arXiv 2023年

作者： Xu, Minrui Du, Hongyang Niyato, Dusit Kang, Jiawen Xiong, Zehui Mao, Shiwen Han, Zhu Jamalipour, Abbas Kim, Dong In Shen, Xuemin Leung, Victor C.M. Poor, H. Vincent The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The School of Automation Guangdong University of Technology Key Laboratory of Intelligent Information Processing and System Integration of IoT Ministry of Education Guangzhou510006 China Guangdong-HongKong-Macao Joint Laboratory for Smart Discrete Manufacturing Guangzhou510006 China The Pillar of Information Systems Technology and Design Singapore University of Technology and Design Singapore487372 Singapore The Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States The Department of Electrical and Computer Engineering University of Houston HoustonTX77004 United States The Department of Computer Science and Engineering Kyung Hee University Seoul446-701 Korea Republic of The School of Electrical and Information Engineering University of Sydney SydneyNSW2006 Australia The Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of The Department of Electrical and Computer Engineering University of Waterloo WaterlooONN2L 3G1 Canada The College of Computer Science and Software Engineering Shenzhen University Shenzhen518061 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverBCV6T 1Z4 Canada The Department of Electrical and Computer Engineering Princeton University PrincetonNJ08544 United States

Artificial Intelligence-Generated Content (AIGC) is an automated method for generating, manipulating, and modifying valuable and diverse data using AI algorithms creatively. This survey paper focuses on the deployment of AIGC applications, e.g., ChatGPT and Dall-E, at mobile edge networks, namely mobile AIGC networks, that provide personalized and customized AIGC services in real time while maintaining user privacy. We begin by introducing the background and fundamentals of generative models and the lifecycle of AIGC services at mobile AIGC networks, which includes data collection, training, fine-tuning, inference, and product management. We then discuss the collaborative cloud-edge-mobile infrastructure and technologies required to support AIGC services and enable users to access AIGC at mobile edge networks. Furthermore, we explore AIGC-driven creative applications and use cases for mobile AIGC networks. Additionally, we discuss the implementation, security, and privacy challenges of deploying mobile AIGC networks. Finally, we highlight some future research directions and open issues for the full realization of mobile AIGC networks. © 2023, CC BY.

关键词： Life cycle

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Narrowband-internet of things (NB-IoT) system for elderly healthcare services 5

Narrowband-internet of things (NB-IoT) system for elderly he...

引用

5th International Conference on Engineering, Applied Sciences and technology, ICEAST 2019

作者： Manatarinat, Wiraphon Poomrittigul, Suvit Tantatsanawong, Panjai Electrical Engineering Department Faculty of Engineering Pathumwan Institute of Technology Bangkok Thailand Software Engineering and Information System Department Faculty of Science and Technology Pathumwan Institute of Technology Bangkok Thailand Computer Technology Department Faculty of Science Silpakorn University Bangkok Thailand

ISBN: (纸本)9781728100678

3G and 4G wireless communication technology, which requires broad network resources and large amounts of available electricity, was designed for high-speed Internet usage. Most narrowband-Internet of Things (NB-IoT) devices do not require high-speed Internet because most IoTs work by sending commands to and receiving commands from a server only. NB-IoT is a low-power wide area (LPWA) technology that was developed to accommodate a broad variety of new IoT devices. A low-power wide area network (LPWAN) uses narrow frequencies that support the use of devices in the 900 MHz frequency range that are inexpensive and that can be used in both buildings and wide areas. NB-IoT devices can also be used to assist medical patients, the elderly, and disabled people that are susceptible to falling. Our newly developed monitoring system has the ability to immediately recognize a fall, and then automatically alert up to 4 contacts via the LINE application. If dispatch of a medical unit is required, the patient's location is automatically available without the need for voice contact by the patient. © 2019 IEEE.

关键词： Internet of things

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A Review on Feature Based Approach in Semantic Similarity for Multiple Ontology 2nd

A Review on Feature Based Approach in Semantic Similarity fo...

引用

2nd International Conference on Advanced Data and information Engineering, DaEng 2015

作者： Omar, Nurul Aswa Kasim, Shahreen Md Fudzee, Mohd. Farhan Department Web Technology Faculty Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Software Multimedia Center Faculty Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia Department Multimedia Faculty Computer Sciences and Information Technology Universiti Tun Hussein Onn Malaysia Parit Raja Malaysia

ISBN: (纸本)9789811317972

Measuring semantic similarity between terms is an important step in information retrieval and information integration which requires semantic content matching. Semantic similarity has attracted great concern for a long time in artificial intelligence, psychology and cognitive science. This paper contains a review of the state of art approaches including structure based approach, information content based approach, features based approach and hybrid approach. We also discussed similarity according to their advantages, disadvantages and issues related to multiple ontology especially on methods in features based approach. © Springer Nature Singapore Pte Ltd. 2019.

关键词： Ontology

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