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International Conference on Data Engineering

作者： Xi Wang Ruochun Jin Wanrong Huang Yuhua Tang Institute for Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China Department of Intelligent Data Science College of Computer Science and Technology National University of Defense Technology Changsha China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Functional dependencies (FDs) form a valuable ingredient for various data management tasks. However, existing methods can hardly discover practical and interpretable FDs, especially in large noisy real-life datasets. This paper studies the problem of discovering meaningful functional dependencies (FDms) that utilize support and error parameters to capture interesting dependencies in such datasets and proposes an efficient discovery algorithm called FDMε. In order to scale with large datasets, FDM ε employs an efficient sampling method with accuracy guarantees to capture the differences between tuple pairs and to quantify the connection between support/error of dependencies on samples and those on the entire dataset. Moreover, it adopts a clustering-based correlated attributes extraction to divide the exponentially large search space into multiple small sub-spaces and proposes an easy-first traversal strategy with covariance-based guidance that quickly detects candidate dependencies and validates them. Additionally, we prove a covariance lower bound as an additional pruning criterion to reduce the search space. Extensive experiments on real-life and synthetic datasets demonstrate that FDM ε is 14 times faster than existing discovery algorithms on average, up to 31 times, and scales to larger datasets with the least memory cost.

关键词： Accuracy Costs Clustering algorithms Search problems Data engineering Noise measurement Frequency division multiplexing

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Recommender System for Social Media: Research Challenges and Future Applications 5

Recommender System for Social Media: Research Challenges and...

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5th International Conference on Transdisciplinary AI, TransAI 2023

作者： Ge, Mouzhi Pilato, Giovanni Persia, Fabio D'Auria, Daniela Deggendorf Institute of Technology Faculty of European Campus Rottal-Inn Deggendorf Germany Institute for High Performance Computing and Networking National Research Council of Italy Palermo Italy University of l'Aquila Department of Information Engineering Computer Science and Mathematics L'Aquila Italy University of Bozen-Bolzano Faculty of Computer Science Free Bozen-Bolzano Italy

ISBN: (纸本)9798350358018

Social networks have evolved as a ubiquitous means of communication in our daily lives. Users consistently generate a large amount of data on social media platforms, which on the other hand has increased the complexity when selecting relevant social media content. Thus, recommender system has been emerged as a solution to provide personalized and user-centric media recommendations. However, social media has significantly broadened the scope of available inputs such as social sensing data and new types of social media. It not only offers new research opportunities for social media recommender systems, but also indicates that various domains may benefit from the application of social media recommender systems. Therefore, this paper conducts a comprehensive survey of state-of-the-art research between social media and recommender systems. The survey results identify a set of research challenges on how social media recommender systems can be applied in various domains such as healthcare and route planning. © 2023 IEEE.

关键词： Recommender systems

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Movable surface acoustic wave tweezers: a versatile toolbox for micromanipulation

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Microsystems & Nanoengineering 2024年第5期10卷 367-380页

作者： Xianming Qin Xianglian Liu Shuo Liu Chuanyu Zhang Ningning Bai Xue Li Weidong Wang Dan Liu Qiqi Yang Ruiguo Yang Yajing Shen Xueyong Wei School of Mechano-Electronic Engineering Xidian UniversityXi'an 710071China State Key Laboratory for Manufacturing Systems Engineering Xi'an Jiaotong UniversityXi'an 710049China State Key Laboratory of Electromechanical Integrated Manufacturing of High-Performance Electronic Equipment Xidian UniversityXian 710071China State Key Laboratory of Holistic Integrative Management of Gastrointestinal Cancers and National Clinical Research Center for Digestive Diseases Xijing Hospital of Digestive DiseasesFourth Military Medical UniversityXi'an 710032China Department of Biomedical Engineering and Institute for Quantitative Health Science and Engineering(IQ)Michigan State UniversityEast LansingMI 48824USA Department of Electronic and Computer Engineering The Hong Kong University of Science and TechnologyClear Water BayKowloonHong KongChina

Surface acoustic wave(SAW)tweezers are a promising multifunctional micromanipulation method that controls microscale targets via patterned acoustic *** to their device structure and bonding process,most SAW tweezers have limitations in terms of controlling the position and motion of the acoustic traps,as they generate an acoustic field with a fixed region and adjust the manipulation effects via signal *** address this challenge,we propose movable SAW tweezers with a multilayer structure,achieving dynamic control of their wave field and acoustic trap positions;we demonstrate their precise manipulation functions,such as translation,in-plane rotation,out-of-plane rotation,and cluster formation,on a wide spectrum of samples,including particles,bubbles,droplets,cells,and *** method not only improves the degree of freedom and working range of SAW tweezers but also allows for precise and selective manipulation of microtargets via microtools and localized *** to their flexibility,versatility,and biocompatibility,the movable SAW tweezers can be a practical platform for achieving arbitrary manipulation of microscale targets and have the potential to play significant roles in biomedical microrobotics.

关键词： wave precise rotation

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Deep Learning Based End-to-End Specular Reflection Removal for Medical Endoscopic Images 23

Deep Learning Based End-to-End Specular Reflection Removal f...

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2023 Research in Adaptive and Convergent Systems, RACS 2023

作者： Shih, Chi-Sheng Liao, Yu-Cheng Tan, Ching-Ting Graduate Institute of Networking and Multimedia Department of Computer Science and Information Engineering High Performance and Scientific Computing Center National Taiwan University Taipei Taiwan Graduate Institute of Networking and Multimedia National Taiwan University Taipei Taiwan Department of Otolaryngology National Taiwan University Hospital Taipei Taiwan

ISBN: (纸本)9798400702280

Endoscopic images usually have many overexposed regions due to strong and focused light sources and, consequently, physicians need to change the camera angle for a clear view from time to time. This work targets removing the specular reflections from the endoscopy images but maintaining the structural and semantic texture in the images. Unlike the state-of-the-art image inpainting methods, the proposed method treats the specular reflection removal problem as an image-to-image translation task by skipping the specular detection step. The technique uses Dense Multi-scale Fusion Network (DMFN), which aims for small size and clear images, as the base and revises the network to support the large size and noisy endoscopy images. Moreover, the method can recover structure from large overexposed regions. Experimental results show that under different glare ratios and granularity settings, it is more effective than the Partial Convolution Model when recovering the corrupted pixels: the mean average error of pixel values decreases 13.2%, and the PSNR value increases 2.62dB. Qualitative analysis shows that even in the worst case when the proposed model generates output images with MAE up to 4.30, its recoveries still possess good visual qualities. The proposed model can process a 480 × 768 image in 16.2ms on a computer equipped with 36 CPU cores and an RTX-3090 GPU. It usually takes at least 20 minutes for a human to recover such images. © 2023 ACM.

关键词： Endoscopy

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Multi-scale Progressive Feature Embedding for Accurate NIR-to-RGB Spectral Domain Translation

arXiv

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arXiv 2023年

作者： Yang, Xingxing Chen, Jie Yang, Zaifeng Department of Computer Science Hong Kong Baptist University Hong Kong Institute of High Performance Computing Agency for Science Technology and Research Singapore

NIR-to-RGB spectral domain translation is a challenging task due to the mapping ambiguities and existing methods show limited learning capacities. To address these challenges, we propose to colorize NIR images via a multi-scale progressive feature embedding network (MPFNet), with the guidance of grayscale image colorization. Specifically, we first introduce a domain translation module that translates NIR source images into the grayscale target domain. By incorporating a progressive training strategy, the statistical and semantic knowledge from both task domains are efficiently aligned with a series of pixel-/feature-level consistency constraints. Besides, a multi-scale progressive feature embedding network is designed to improve learning capabilities. Experiments show that our MPFNet outperforms state-of-the-art counterparts by 2.55dB in the NIR-to-RGB spectral domain translation task in terms of PSNR. Copyright © 2023, The Authors. All rights reserved.

关键词： Semantics

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Hyperspectral Image Reconstruction via Combinatorial Embedding of Cross-Channel Spatio-Spectral Clues

arXiv

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arXiv 2023年

Existing learning-based hyperspectral reconstruction methods show limitations in fully exploiting the information among the hyperspectral bands. As such, we propose to investigate the chromatic inter-dependencies in their respective hyperspectral embedding space. These embedded features can be fully exploited by querying the inter-channel correlations in a combinatorial manner, with the unique and complementary information efficiently fused into the final prediction. We found such independent modeling and combinatorial excavation mechanisms are extremely beneficial to uncover marginal spectral features, especially in the long wavelength bands. In addition, we have proposed a spatio-spectral attention block and a spectrum-fusion attention module, which greatly facilitates the excavation and fusion of information at both semantically long-range levels and fine-grained pixel levels across all dimensions. Extensive quantitative and qualitative experiments show that our method (dubbed CESST) achieves SOTA performance. Code for this project is at: https://***/AlexYangxx/CESST. Copyright © 2023, The Authors. All rights reserved.

关键词： Excavation

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LprViT:An Efficient ViT for LiDAR-Based Place Recognition Using Range Image

LprViT:An Efficient ViT for LiDAR-Based Place Recognition Us...

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Communication, Image and Signal Processing (CCISP), International Conference on

作者： Rui He Yunzhe Xiao Shaowu Yang Quantum Information & State Key Laboratory of High Performance Computing College of Computer Science and Technology National University of Defense Technology Changsha China

Place recognition is a crucial component in the realm of simultaneous localization and mapping (SLAM) and autonomous driving. It can also enhance the performance of loop closure detection and global localization. LiDAR-based place recognition usually relies on global feature descriptors to accurately express the similarity between LiDAR scans. However, it is time-consuming to extract and generate the global feature descriptors from the original LiDAR data. As a result, treating LiDAR-based point clouds place recognition as a 2D image problem using range projection is becoming an increasingly common and successful strategy. Vision Transformer (ViT) is a Transformer model based on self-attention mechanism for image classification. Through self-attention mechanism, ViT out-performs CNN at modeling the global information of images. We investigate that whether ViT can be transferred to LiDAR-based place recognition tasks. Firstly, we introduce a new convolutional stem to replace the original linear embedding layer in ViT. Secondly, we establish synergy by integrating low-level fine-grained features from new convolution layer with high-level coarse-grained features from ViT by skip connections. Thirdly, we aggregate these features to construct the global feature descriptor via the NetVLAD technique. The results of experiments on KITTI confirm that our method can operate online and achieve superior performance on par with existing approaches.

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Data Logging and Non-invasive IoMT Approach for Rats Monitoring in laboratory Experiments 37th

Data Logging and Non-invasive IoMT Approach for Rats Monit...

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37th International Conference on Advanced Information Networking and Applications, AINA 2023

作者： Barbosa, Bryan C. M. Garan, Steven A. Quintela, Barbara Melo Dantas, Mario R. Universidade Federal de Juiz de Fora Juiz de Fora Brazil Center for Research and Education in Aging University of California Berkeley and Lawrence Berkeley National Laboratory BerkeleyCA United States Computational Physiology and High Performance Computing Laboratory and Computer Science Department at Universidade Federal de Juiz de Fora Juiz de Fora Brazil Computer Science Graduate Program at Universidade Federal de Juiz de Fora Juiz de Fora Brazil

ISBN: (纸本)9783031290558

It is being observed that the use of Internet of Medical Things (IoMT) in health sciences research grows as the technology and miniaturization of devices occur. Those devices often times suffer from several issues such as: being invasive, lacking the ability to collect many signals concurrently and communication. The present work proposes an approach to the use of an ultra-small IoMT device to fulfill the functions of a bio-signals data collector, a mathematical models processor, a data transmitter-receptor through the network, and a mechanical actuators controller. Our test environment was conceived to be used in animal models. This is an ongoing project from which partial results indicate that, when functioning, the device will use real-time data for monitoring bio-signals and helping with decision-making as it interacts with the organism. The data collection is going to be made through low-cost non-invasive sensors. Therefore, it can favor installation, maintenance, access, and promote a better quality of life for the user. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Decision making

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Efficient Collusion-Resisting Secure Sum Protocol

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Chinese Journal of Electronics 2023年第3期20卷 407-413页

作者： Youwen ZHU Liusheng HUANG Wei YANG Xing YUAN Department of Computer Science and Technology National High Performance Computing Center at Hefei University of Science and Technology of China Hefei China Suzhou Institute for Advanced Study University of Science and Technology of China Suzhou China

Secure sum protocol is a significant secure multiparty computation protocol and it has various applications in privacy-preserving distributed multiparty computation. However, most existing secure sum protocols rarely considered how to resist underlying collusion which is a significant practical problem. Urabe et al. proposed a collusion-resistant secure sum protocol, but too much cost of communication and computation results in its low performance efficiency. In this paper, we propose security definitions to measure secure multiparty computation protocol's capability of resisting potential collusion. Then, we precisely analyze several previous secure sum protocols' capability of resisting collusion. In addition, considering realistic requirement to resist collusion and performance efficiency needs, we present a novel collusion-resisting secure sum protocol. Theoretical analysis and experimental results confirm that our secure sum protocol is efficient and has strong capability of resisting potential collusion such that it is much superior to previous ones. The communication overheads and computation complexity of our scheme both are linearity of the number of participants. Besides, our protocol's capability of resisting collusion is adjustable according to different security needs.

关键词： Protocols Costs Linearity Resists Computational efficiency Security Computational complexity

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Interacting plexcitons for designed ultrafast optical nonlinearity in a monolayer semiconductor

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Light(science & Applications) 2022年第5期11卷 877-887页

作者： Yuxiang Tang Yanbin Zhang Qirui Liu Ke Wei Xiang’ai Cheng Lei Shi Tian Jiang College of Advanced Interdisciplinary Studies National University of Defense Technology410073 ChangshaChina Key Laboratory of Micro-and NanoPhotonic Structures(Ministry of Education) and State Key Laboratory of Surface PhysicsDepartment of PhysicsFudan University200433 ShanghaiChina State Key Laboratory of High Performance Computing College of ComputerNational University of Defense Technology410073 ChangshaChina Beijing Institute for Advanced Study National University of Defense Technology100000 BeijingChina

Searching for ideal materials with strong effective optical nonlinear responses is a long-term task enabling remarkable breakthroughs in contemporary quantum and nonlinear ***,hybridized light-matter quasiparticles,are an appealing candidate to realize such ***,we explore a class of peculiar polaritons,named plasmon–exciton polaritons(plexcitons),in a hybrid system composed of silver nanodisk arrays and monolayer tungsten-disulfide(WS2),which shows giant room-temperature nonlinearity due to their deep-subwavelength localized ***,comprehensive ultrafast pump–probe measurements reveal that plexciton nonlinearity is dominated by the saturation and higher-order excitation-induced dephasing interactions,rather than the well-known exchange interaction in traditional microcavity ***,we demonstrate this giant nonlinearity can be exploited to manipulate the ultrafast nonlinear absorption properties of the solid-state *** findings suggest that plexcitons are intrinsically strongly interacting,thereby pioneering new horizons for practical implementations such as energy-efficient ultrafast all-optical switching and information processing.

关键词： nonlinearity monolayer nonlinear

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