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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Su, Yawen Bai, Jing Lin, Gan School of Computer Science and Engineering North Minzu University Yinchuan China The Key Laboratory of Images & Graphics Intelligent Processing of State Ethnic Affairs Commission China

ISBN: (纸本)9798350368741

Sketch-based 3D shape retrieval has become a prominent area of research in computer vision, confronting challenges related to the inherent diversity and abstraction of sketches, as well as inter-domain discrepancies. This paper introduces a novel approach called Dual Knowledge Distillation Dynamic Learning (DKD2L) aimed at enhancing the extraction of spatio-temporal features for sketch-based 3D shape retrieval. We develop a temporal feature extraction network to effectively capture the dynamic temporal characteristics of sketches and improve retrieval efficiency through temporal knowledge distillation. Additionally, to tackle intra-class variation and inter-class imbalance, we apply semantic knowledge distillation, enabling the 3D shape network to guide the sketch network in capturing common semantic information. This approach facilitates precise cross-modal alignment and enhances retrieval accuracy. Extensive experiments on two benchmark datasets demonstrate that DKD2L surpasses existing state-of-the-art methods. © 2025 IEEE.

关键词： 3D Shape Retrieval Knowledge Distillation Sketch-based 3D Shape Retrieval Temporal Feature

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Cavity-agnostic acoustofluidic manipulations enabled by guided flexural waves on a membrane acoustic waveguide actuator

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Microsystems & Nanoengineering 2024年第2期10卷 153-171页

作者： Philippe Vachon Srinivas Merugu Jaibir Sharma Amit Lal Eldwin J.Ng Yul Koh Joshua E.-Y.Lee Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Institute of Microelectronics Agency for ScienceTechnology and Research(A*STAR)SingaporeSingapore SonicMEMS Laboratory School of Electrical and Computer EngineeringCornell UniversityIthacaNYUSA School of Electrical and Data Engineering University of Technology SydneyUltimoNSWAustralia

This article presents an in-depth exploration of the acoustofluidic capabilities of guided flexural waves(GFWs)generated by a membrane acoustic waveguide actuator(MAWA).By harnessing the potential of GFWs,cavity-agnostic advanced particle manipulation functions are achieved,unlocking new avenues for microfluidic systems and lab-on-a-chip *** localized acoustofluidic effects of GFWs arising from the evanescent nature of the acoustic fields they induce inside a liquid medium are numerically investigated to highlight their unique and promising *** traditional acoustofluidic technologies,the GFWs propagating on the MAWA’s membrane waveguide allow for cavity-agnostic particle manipulation,irrespective of the resonant properties of the fluidic ***,the acoustofluidic functions enabled by the device depend on the flexural mode populating the active region of the membrane *** demonstrations using two types of particles include in-sessile-droplet particle transport,mixing,and spatial separation based on particle diameter,along with streaming-induced counter-flow virtual channel generation in microfluidic PDMS *** experiments emphasize the versatility and potential applications of the MAWA as a microfluidic platform targeted at lab-on-a-chip development and showcase the MAWA’s compatibility with existing microfluidic systems.

关键词： waveguide fluid cavity

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SV-HaRF: a scene representation method for sparse image view synthesis

SV-HaRF: a scene representation method for sparse image view...

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2023 International Conference on computer graphics, Artificial Intelligence, and Data Processing, ICCAID 2023

作者： Liu, Wei Zhang, Yu Zou, Xuechao Ma, Rui Tao, Pin Department of Computer Technology and Applications Qinghai University China Department of Computer Science and Technology Tsinghua University China Key Laboratory of Pervasive Computing Ministry of Education China

ISBN: (纸本)9781510677685

Existing view synthesis algorithms often require dozens or even hundreds of photographs taken from different angles to capture more intricate details in the resulting 3D model. While increasing the number of angles can yield better model parameters, it also significantly increases the training cost. Therefore, there is a need to reduce the quantity of input images with different angles while maintaining comparable results. In this paper, we propose SV-HaRF, which aims to reconstruct scenes by using a sparse set of angles (typically less than 100 but not less than 10) to ensure scene model integrity, as the original training sets often consist of over 100 angles. To address this issue, SV-HaRF employs hash encoding on the input sparse image resolutions to enhance the spatial constraint of these viewpoint positions during the scene reconstruction process, thereby improving the ability to restore scene information. SV-HaRF not only demonstrates outstanding performance in synthesizing sparse viewpoints for reducing model angles but also exhibits superior enhancement, compared to existing NeRF and its related variants, when the input image viewpoints remain unchanged. We conducted experiments on the NeRF Blender Synthetic dataset, showcasing significant improvements in scene synthesis achieved by SV-HaRF. © 2024 SPIE.

关键词： Image resolution

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From Missing Pieces to Masterpieces: Image Completion with Context-Adaptive Diffusion

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IEEE Transactions on Pattern Analysis and Machine Intelligence 2025年第7期47卷 6073-6087页

作者： Shamsolmoali, Pourya Zareapoor, Masoumeh Zhou, Huiyu Felsberg, Michael Tao, Dacheng Li, Xuelong University of York Department of Computer Science United Kingdom University of Leicester School of Computing and Mathematical Sciences United Kingdom Linkoping University Computer Vision Laboratory Sweden Nanyang Technological University College of Computing & Data Science Singapore China Northwestern Polytechnical University Key Laboratory of Intelligent Interaction and Applications Ministry of Industry and Information Technology Xi'an China

Image completion is a challenging task, particularly when ensuring that generated content seamlessly integrates with existing parts of an image. While recent diffusion models have shown promise, they often struggle with maintaining coherence between known and unknown (missing) regions. This issue arises from the lack of explicit spatial and semantic alignment during the diffusion process, resulting in content that does not smoothly integrate with the original image. Additionally, diffusion models typically rely on global learned distributions rather than localized features, leading to inconsistencies between the generated and existing image parts. In this work, we propose ConFill, a novel framework that introduces a Context-Adaptive Discrepancy (CAD) model to ensure that intermediate distributions of known and unknown regions are closely aligned throughout the diffusion process. By incorporating CAD, our model progressively reduces discrepancies between generated and original images at each diffusion step, leading to contextually aligned completion. Moreover, ConFill uses a new Dynamic Sampling mechanism that adaptively increases the sampling rate in regions with high reconstruction complexity. This approach enables precise adjustments, enhancing detail and integration in restored areas. Extensive experiments demonstrate that ConFill outperforms current methods, setting a new benchmark in image completion. © 1979-2012 IEEE.

关键词： computer aided design

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A Survey of Edge Caching:Key Issues and Challenges

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Tsinghua science and Technology 2024年第3期29卷 818-842页

作者： Hanwen Li Mingtao Sun Fan Xia Xiaolong Xu Muhammad Bilal School of Computer Science Nanjing University of Information Science and TechnologyNanjing 210044China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 262700China Reading Academy Nanjing University of Information Science and TechnologyNanjing 210044China School of Software Nanjing University of Information Science and TechnologyNanjing 210044China Department of Computer Engineering Hankuk University of Foreign StudiesYongin-si 17035Republic of Korea

With the rapid development of mobile communication technology and intelligent applications,the quantity of mobile devices and data traffic in networks have been growing exponentially,which poses a great burden to networks and brings huge challenge to servicing user *** caching,which utilizes the storage and computation resources of the edge to bring resources closer to end users,is a promising way to relieve network burden and enhance user *** this paper,we aim to survey the edge caching techniques from a comprehensive and systematic *** first present an overview of edge caching,summarizing the three key issues regarding edge caching,i.e.,where,what,and how to cache,and then introducing several significant caching *** then carry out a detailed and in-depth elaboration on these three issues,which correspond to caching locations,caching objects,and caching strategies,*** particular,we innovate on the issue“what to cache”,interpreting it as the classification of the“caching objects”,which can be further classified into content cache,data cache,and service ***,we discuss several open issues and challenges of edge caching to inspire future investigations in this research area.

关键词： edge caching edge computing caching location caching object caching strategy 5G networkarchitecture Internet of Things(IoT)

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Precision Unleashed: Enhancing Vital Sign Predictions Using Long-Short-Term Memory Networks

Precision Unleashed: Enhancing Vital Sign Predictions Using ...

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2023 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2023

作者： Dakhia, Zohra Belouaar, Houcine Belaiche, Leyla Kahloul, Laid University of Biskra Linfi Laboratory Department of Computer Science Biskra Algeria

ISBN: (纸本)9798350307771

Precise vital sign prediction is essential to monitor and treat patients' medical conditions. This paper provides a deep learning-based method using Long-Short-Term Memory (LSTM) networks to forecast the values of vital signs, particularly the body temperature and heart rate. To conduct a comparative study, the performance of the proposed LSTM model is compared to other prediction models, such as statistics and deep neural network models. The models' performance assessment was performed using two vital sign datasets: the body temperature dataset from a publicly accessible dataset and the heart rate dataset from the MIT-BIH Arrhythmia Database. To determine how accurately the proposed model predictions were made, three evaluation measures are considered: mean squared error (MSE), mean absolute error (MAE), and root mean squared error (RMSE). The experimental results illustrate that the proposed LSTM-based model outperforms the industry standard models in decreasing the prediction errors for heart rate and body temperature. The results of this study demonstrate how well LSTM networks capture temporal connections and accurately predict vital sign readings. Besides, the proposed strategy attains the potential boosting of patient monitoring systems and the standard of healthcare delivery. © 2023 IEEE.

关键词： Long short-term memory

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A Survey and Experimental Review on Data Distribution Strategies for Parallel Spatial Clustering Algorithms

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Journal of computer science & Technology 2024年第3期39卷 610-636页

作者： Jagat Sesh Challa Navneet Goyal Amogh Sharma Nikhil Sreekumar Sundar Balasubramaniam Poonam Goyal Advanced Data Analytics and Parallel Technologies Laboratory Birla Institute of Technology and Science Pilani 333031India Uber New York 11101U.S.A. Computer Science and Engineering Department University of MinnesotaMinneapolis 55455U.S.A.

The advent of Big Data has led to the rapid growth in the usage of parallel clustering algorithms that work over distributed computing frameworks such as MPI,MapReduce,and *** important step for any parallel clustering algorithm is the distribution of data amongst the cluster *** step governs the methodology and performance of the entire *** typically use random,or a spatial/geometric distribution strategy like kd-tree based partitioning and grid-based partitioning,as per the requirements of the ***,these strategies are generic and are not tailor-made for any specific parallel clustering *** this paper,we give a very comprehensive literature survey of MPI-based parallel clustering algorithms with special reference to the specific data distribution strategies they *** also propose three new data distribution strategies namely Parameterized Dimensional Split for parallel density-based clustering algorithms like DBSCAN and OPTICS,Cell-Based Dimensional Split for dGridSLINK,which is a grid-based hierarchical clustering algorithm that exhibits efficiency for disjoint spatial distribution,and Projection-Based Split,which is a generic distribution *** of these preserve spatial locality,achieve disjoint partitioning,and ensure good data load *** experimental analysis shows the benefits of using the proposed data distribution strategies for algorithms they are designed for,based on which we give appropriate recommendations for their usage.

关键词： parallel data mining data distribution parallel clustering spatial locality preservation

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A Robust Misbehavior Detection System for Cooperative Driving Network 12

A Robust Misbehavior Detection System for Cooperative Drivin...

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12th International Symposium on Digital Forensics and Security, ISDFS 2024

作者： Abdullahi, Abdullahi Modibbo Lalouani, Wassila Rahim, Messaoud Towson University Department of Computer and Information Science TowsonMD United States University of Medea Mathematics and Computer Sciences Department Algeria Usthb Movep Laboratory Algeria

ISBN: (纸本)9798350330366

Cooperative Intelligent Transportation Systems (c-ITS) stand as visionary pillars in shaping the future landscape of vehicular networks. These systems hinge on the seamless orchestration of Vehicle-to-everything (V2X) communication, fostering the seamless exchange of vital information between vehicles and the underlying infrastructure. Within this intricate ecosystem, vehicles collaborate harmoniously, disseminating V2X messages throughout the network. However, the transmission of deceptive or inaccurate information by rogue vehicles has the potential to endanger road safety. Misbehavior Detection systems (MBS) perform plausibility and consistency checks on the received V2X information or consider machine learning models training while ignoring the variability of the traffic over time. In this paper, we introduce a stacked meta-learning model for misbehavior detection, leveraging time-series V2X data to distinguish between genuine and aberrant exchanged information. Our proposed approach integrates predictions from various probabilistic learning models, incorporating them into a CNN-LSTM meta-learner to weigh the outputs of the probabilistic classifiers for accurate misbehavior detection. Our method demonstrates its ability to identify a wide range of faults and attacks, including those associated with rare types of attack, by learning the inherent data distribution and essential characteristics of network traffic. Simulation results reveal that the proposed model not only outperforms prominent MBS in the literature by accurately identifying various forms of misbehavior within cooperative driving systems but also reduces the overall execution time of aberrant behavior detection. © 2024 IEEE.

关键词： Learning systems

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Efficient Dilution of a Fluid from its Related Arbitrary Stock Solutions using MEDA Biochips 19

Efficient Dilution of a Fluid from its Related Arbitrary Sto...

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19th IEEE Asia Pacific Conference on Circuits and Systems, APCCAS 2023

作者： Aditya V. Dupukuntla, Surya Naga Nimmaturi, Koushik Sai Mandal, Tamal Abramoni, Sathwik Roy, Sudip Iit CoDA Laboratory Department of Computer Science and Engineering Roorkee India

ISBN: (纸本)9798350381191

Micro-electrode-dot-array (MEDA) based digital microfluidic biochips have been developed for automated execution of bioprotocols, which are used in point-of-care diagnostics. In literature, many sample preparation algorithms are reported for determining the protocol graph (called as dilution tree) for automated dilution and mixing of two or more input fluids, which are pure and having infinite supplies. For dilution of a fluid from its related arbitrary stock solutions (RASS), out of existing methods GDA (Roy et al., JETC, 2014) uses only (1: 1) mixing model, and hRASS (Ghosh et al., ISDCS, 2022) uses various mixing models of maximum mixer-M (M is a positive integer) and every mixer is of the same size at each mixing node. A drawback of hRASS is to always use only one droplet of intermediate fluids to determine the dilution tree. In this paper, we propose a satisfiability modulo theory (SMT) based optimization technique to determine the dilution tree for a RASS problem instance having two criteria, where (a) the input fluids are expensive and overall cost is to be minimized (named as cRASS), and (b) the volume-wise availability of the input solutions are restricted (named as vRASS). Simulation results validate that using MEDA biochips with various mixer size constraints (M), cRASS can overcome the drawback of hRASS by reusing more than one droplet of intermediate fluids and determine the cost-effective dilution tree, whereas vRASS can determine the dilution tree for some of the problem instances when hRASS and cRASS cannot provide any solution under availability restrictions. © 2023 IEEE.

关键词： Mixers (machinery)

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MDGCN-Lt:Fair Web API Classification with Sparse and Heterogeneous Data Based on Deep GCN

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清华大学学报自然科学版（英文版） 2025年第3期30卷 1294-1314页

作者： Boyuan Yan Yankun Zhang Wenwen Gong Haoyang Wan Wenwei Wang Weiyi Zhong Caixia Bu Department of School of Computer Science Qufu Normal UniversityRizhao 276500China Shandong Provincial University Laboratory for Protected Horticulture Weifang University of Science and TechnologyWeifang 261000China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China Shandong Provincial Key Laboratory of Network Based Intelligent Computing University of JinanJinan 250022China Department of Univalsoft Joint Stock Co. Ltd.Weifang 261000China

Developers integrate web Application Programming Interfaces(APIs)into edge applications,enabling data expansion to the edge computing area for comprehensive coverage of devices in that *** develop edge applications,developers search API categories to select APIs that meet specific ***,the accurate classification of APIs becomes critically ***,existing approaches,as evident on platforms like ***,face significant ***,sparsity in API data reduces classification accuracy in works focusing on single-dimensional API ***,the multidimensional and heterogeneous structure of web APIs adds complexity to data mining tasks,requiring sophisticated techniques for effective integration and analysis of diverse data ***,the long-tailed distribution of API data introduces biases,compromising the fairness of classification *** these challenges,we propose MDGCN-Lt,an API classification approach offering flexibility in using multi-dimensional heterogeneous *** tackles data sparsity through deep graph convolutional networks,exploring high-order feature interactions among API ***-Lt employs a loss function with logit adjustment,enhancing efficiency in handling long-tail data *** results affirm our approach's superiority over existing methods.

关键词： bidirectional encoder representations from transformers deep graph convolutional networks logit adjustment web application programming interface classification application programming interface correlation graph

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