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Light field depth estimation:A comprehensive survey from principles to future

High-Confidence Computing 2024年第1期4卷 92-107页

作者： Tun Wang Hao Sheng Rongshan Chen Da Yang Zhenglong Cui Sizhe Wang Ruixuan Cong Mingyuan Zhao State Key Laboratory of Virtual Reality Technology and Systems School of Computer Science and EngineeringBeihang UniversityBeijing 100191China Zhongfa Aviation Institute Beihang UniversityHangzhou 311115China Faculty of Applied Sciences Macao Polytechnic UniversityMacao 999078China State Key Laboratory of Software Development Environment School of Computer Science and EngineeringBeihang UniversityBeijing 100191China

Light Field(LF)depth estimation is an important research direction in the area of computer vision and computational photography,which aims to infer the depth information of different objects in threedimensional scenes by capturing LF *** this new era of significance,this article introduces a survey of the key concepts,methods,novel applications,and future trends in this *** summarize the LF depth estimation methods,which are usually based on the interaction of radiance from rays in all directions of the LF data,such as epipolar-plane,multi-view geometry,focal stack,and deep *** analyze the many challenges facing each of these approaches,including complex algorithms,large amounts of computation,and speed *** addition,this survey summarizes most of the currently available methods,conducts some comparative experiments,discusses the results,and investigates the novel directions in LF depth estimation.

关键词： Light field Depth estimation Deep learning Sub-aperture image Epipolar-plane image

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Middle school Architect: Programmatic Modeling and Construction via a 3D Graphics Platform in Elm 2

Middle School Architect: Programmatic Modeling and Construct...

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2nd International Conference on Emerging Trends in Information technology and engineering, ic-ETITE 2024

作者： Parcha, Anurag Mishra, Sanjana Anand, Christopher Kumar Pushparaj, Jagalingam Simon Fraser University BurnabyBC Canada McMaster University Computing and Software HamiltonON Canada School of Computer Science and Engineering Vellore Institute of Technology TN Vellore India

ISBN: (纸本)9798350328202

Today, school students are first introduced to geom-etry and advanced mathematical concepts by plotting a graph in 2D and they struggle with 3D concepts since current technologies are either too difficult to grasp or too simple with only drag-drop functionalities disconnected from coordinates. This article presents a 3D graphics library and an interactive, educational system for visualization of a 3D environment where planks of Cross Laminated Timber (CLT) can be manipulated by geometric and mathematical functions to produce a 3D building. Throughout, there is a focus on functions, 3D geometry and other underlying mathematics, and links are made with the world of computing, specifically computer graphics, to increase students' interest and confidence. Activities built on this library simultaneously meet multiple curricular objectives: mathematics (including 3D geometry), programming (in Elm, a web program-ming language used commercially), and sustainable development (using a negative embodied carbon building material). © 2024 IEEE.

关键词： Sustainable development

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Adaptive Coordination with Graph Convolution Networks and Grey Wolf Optimization for Next-Gen 5G Network Slicing

Adaptive Coordination with Graph Convolution Networks and Gr...

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2024 International Conference on Fuzzy Theory and Its Applications, iFUZZY 2024

作者： Kowsalya, M. Ra, In-Ho Naveenraj, K.S. Thruvenkadam, S. Park, Seok-Woo School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan Korea Republic of Jio Platform Limited Internet Protocol Division Mumbai India

ISBN: (纸本)9798350352788

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches. © 2024 IEEE.

关键词： 5G mobile communication systems

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Design of Multimedia Database Online Integration System Based on Artificial Intelligence 2

Design of Multimedia Database Online Integration System Base...

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2nd IEEE International Conference on Integrated Circuits and Communication Systems, ICICACS 2024

作者： Li, Jiaqi Xiong, Gaojie Wu, Xing Harbin Institute of Technology Computer Sciense and Technology Guangdong Shenzhen China University of Electronic Science and Technology School of Information and Software Engineering Sichuan Chengdu China Zhejiang Vocational Academy of Art Zhejiang Hangzhou China

ISBN: (纸本)9798350317558

Multimedia database online integration system plays an important role in today's information age. However, the performance of the integrated system is limited by the problems of low accuracy, insufficient recall rate and long running time of traditional methods in processing multimedia data. This paper designs an online integration system based on artificial intelligence (AI) and verifies its effectiveness and feasibility through experiments so as to overcome these problems. In the study, multimedia datasets suitable for the experimental needs are selected and the AI-based online integration system is compared with traditional methods. Through the analysis of accuracy, recall, F1 value and mean square error, it is found that the online integration system based on AI has obvious advantages in multimedia data integration. The results of this paper demonstrate the feasibility and effectiveness of an online integration system based on AI. It is able to enhance the accuracy and efficiency of multimedia data integration while reducing runtime and resource consumption. © 2024 IEEE.

关键词： artificial intelligence integration efficiency multimedia database online integration

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Audio-Assisted Smart Home Security Monitoring with Few Samples

Audio-Assisted Smart Home Security Monitoring with Few Sampl...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Chi, Haotian Ma, Qi Zhang, Yuxuan Fu, Chenglong Wang, Yuwei Geng, Haijun Du, Xiaojiang Shanxi University School of Automation and Software Engineering Taiyuan China University of North Carolina at Charlotte Department of Software and Information Systems CharlotteNC United States Stevens Institute of Technology Department of Electical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Smart home IoT devices have always been the target of various cyber attacks. By leveraging the smart home monitoring infrastructure, event-based anomaly detection is effective to detect anomalies that cause unfavorable working state of IoT devices. However, IoT events are proven to be vulnerable to event-targeted attacks which could be achieved by exploiting the vulnerabilities embedded in IoT devices, protocols and/or platforms. Thus, existing event-based anomaly detection is not robust in the case of unreliable input. To address this issue, our insight is that the embedded microphone components in many off-the-shelf home devices (e.g., smart doorbells, speakers, cameras, tablets, laptops, etc.) could be utilized to gather acoustic information to help increase the reliability and capability of smart home security monitoring systems. To verify this idea, we propose an audio-assisted framework IoTAudMon for detecting event-targeted attacks. Considering the heterogeneity and sparsity nature of smart homes IoT devices and events, we employ transfer learning to design a practical pipeline for extracting semantic information from audio, eliminating the requirement of human labeling and mitigating the cold start issue in existing solutions. Experiments on public datasets and real devices demonstrate the effectiveness of IoTAudMon. © 2024 IEEE.

关键词： Smart manufacturing

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Finding the Truth from Uncertain Time Series Based on Entropy

Finding the Truth from Uncertain Time Series Based on Entrop...

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International Conference on Wireless Communications and Signal Processing (WCSP)

作者： Bo Jiang Jizhou Sun School of Computer and Software Engineering Huaiyin Institute of Technology Huaian China

ISBN: (纸本)9781665450867

Truth discovery on time series data aims to estimate true data at each timestamp from measurements of multiple uncertain data sources such as sensors. It has received much attention in recent years and has been applied practical Internet of Things applications. Many truth discovery methods have been proposed for scalar categorical and numerical data. However, very few methods focus on time series data, and require different prior knowledge for different datasets. In this paper, we propose a novel Entropy based Truth Discovery method (ETD). The main idea behind is that dirty data usually has high entropy in frequency domain. The main benefit of our method is that it is adaptive, and there is no need for user to specify parameters manually. Experimental results on real-world datasets demonstrate the superior performance of our approach.

关键词： Wireless communication Soft sensors Frequency-domain analysis Time series analysis Measurement uncertainty Signal processing Entropy

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Alignment efficient image-sentence retrieval considering transferable cross-modal representation learning

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Frontiers of computer Science 2024年第1期18卷 77-91页

作者： Yang YANG Jinyi GUO Guangyu LI Lanyu LI Wenjie LI Jian YANG School of Computer Science and Engineering Nanjing University of Science and TechnologyNanjing 210094China Department of Computing Hong Kong Polytechnic UniversityHong Kong 100872China State Key Lab.for Novel Software Technology Nanjing UniversityNanjing 210094China 14th Research Institute of China Electronics Technology Group Corporation Nanjing 210094China guangyu.li2017@***

Traditional image-sentence cross-modal retrieval methods usually aim to learn consistent representations of heterogeneous modalities,thereby to search similar instances in one modality according to the query from another modality in *** basic assumption behind these methods is that parallel multi-modal data(i.e.,different modalities of the same example are aligned)can be obtained in *** other words,the image-sentence cross-modal retrieval task is a supervised task with the alignments as ***,in many real-world applications,it is difficult to realign a large amount of parallel data for new scenarios due to the substantial labor costs,leading the non-parallel multi-modal data and existing methods cannot be used *** the other hand,there actually exists auxiliary parallel multi-modal data with similar semantics,which can assist the non-parallel data to learn the consistent ***,in this paper,we aim at“Alignment Efficient Image-Sentence Retrieval”(AEIR),which recurs to the auxiliary parallel image-sentence data as the source domain data,and takes the non-parallel data as the target domain *** single-modal transfer learning,AEIR learns consistent image-sentence cross-modal representations of target domain by transferring the alignments of existing parallel ***,AEIR learns the image-sentence consistent representations in source domain with parallel data,while transferring the alignment knowledge across domains by jointly optimizing a novel designed cross-domain cross-modal metric learning based constraint with intra-modal domain adversarial ***,we can effectively learn the consistent representations for target domain considering both the structure and semantic ***,extensive experiments on different transfer scenarios validate that AEIR can achieve better retrieval results comparing with the baselines.

关键词： image-sentence retrieval transfer learning semantic transfer structure transfer

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DPFedSAM-Meas: Comparison Of Differential Privacy Federated Learning In Medical Image Classification

DPFedSAM-Meas: Comparison Of Differential Privacy Federated ...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Jiang, Shunrong Wang, Zhi He, Zixuan Li, Yonggang Li, Xiaofan Chi, Haotian Du, Xiaojiang China University of Mining and Technology School of Computer Science Xuzhou221116 China Jiangsu Jingchuang Electric CO. LTD China Shanxi University School of Automation and Software Engineering Taiyuan030006 China Stevens Institute of Technology Department of Electrical and Computer Engineering HobokenNJ United States

ISBN: (纸本)9798350351255

Machine learning is widely used in medical image classification tasks. However, medical images often exhibit uneven distribution and high sensitivity to noise. A feasible solution involves using federated learning (FL) with differential privacy (DP), a distributed training method that protects patient privacy. In this work, many studies have proposed improved solutions for localized differential-private federated learning (DP-FL) frameworks. However, these studies are isolated, which would be detrimental to privacy practitioners in designing and using the algorithms. To provide a comprehensive analysis of these algorithms, we propose DPFedSAM-Meas, as a framework for comprehensive utility analysis of DP-FL. In this framework, we employed the state-of-the-art federated learning framework FedSAM. Moreover, we categorize DP algorithms into Laplace DP and Gaussian DP by the underlying DP mechanisms, and into Gradient DP and Parameter DP by the DP position in FL train. DPFedSAM-Meas allows a comparative analysis of these four DP techniques, measuring their model utility, privacy leakage, and overhead when FL uses different network structures. Finally, we evaluate DPFedSAM-Meas on datasets of Pneumonia, Blood, and Path, aiming to investigate the performance of different DP techniques on mainstream deep learning algorithms, including Convolutional Neural Networks (CNN) and Vision Transformers (ViT). © 2024 IEEE.

关键词： Federated learning

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Predicting the Unseen: A Novel Dataset for Hidden Intention Localization in Pre-abnormal Analysis 24

Predicting the Unseen: A Novel Dataset for Hidden Intention ...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Qi, Zehao Zhang, Ruixu Hu, Xinyi Liu, Wenxuan Wang, Zheng National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Wuhan University Hubei Key Laboratory of Multimedia and Network Communication Engineering School of Computer Science Wuhan China School of Computer Science and Artificial Intelligence Wuhan University of Technology Wuhan China

ISBN: (纸本)9798400706868

Our paper introduces a novel video dataset specifically for Temporal Intention Localization (TIL), aimed at identifying hidden abnormal intention in densely populated and complex environments. Traditional Temporal Action Localization (TAL) frameworks, focusing on overt actions within constrained temporal intervals, often miss subtle pre-abnormal actions that unfold over extended periods. Our dataset comprises 228 videos with 5790 clips, each annotated to capture fine-grained actions within ambiguous temporal boundaries using the Joint-Linear-Assignment methodology. This approach enables detailed analysis of the evolution of abnormal intention over time. To detect subtle, hidden intention, we developed the Intention-Action Fusion module, an creative approach integrating dynamic feature fusion across 11 behavioral subcategories, significantly enhancing the model's ability to discern nuanced intention. This enhancement has led to performance improvements of up to 139% in specific scenarios, dramatically boosting the model's sensitivity and interpretability, crucial for advancing proactive surveillance systems. By pushing the boundaries of technology, our dataset and methodologies foster proactive surveillance systems capable of preemptively identifying potential threats from nuanced behavioral patterns, encouraging further exploration into the complexities of intention beyond observable actions. The dataset is available at https://***/Zzz99999/Hidden-Abnormal-Intention. © 2024 ACM.

关键词： Security systems

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Towards Efficient Auction Design with ROI Constraints 23

Towards Efficient Auction Design with ROI Constraints

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23rd International Conference on Autonomous Agents and Multiagent Systems, AAMAS 2024

作者： Tang, Xinyu Lv, Hongtao Gao, Yingjie Wu, Fan Liu, Lei Cui, Lizhen School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University Shandong Research Institute of Industrial Technology Jinan China

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3/2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5/4;when it falls below 2, the 3+γ lower bound becomes (Equation presented). © 2024 International Foundation for Autonomous Agents and Multiagent Systems.

关键词： Autonomous agents

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