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Scenarios analysis and performance assessment of blockchain integrated in 6G scenarios

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science China(Information sciences) 2024年第7期67卷 5-26页

作者： Bo LI Guanjie CHENG Honghao GAO Xueqiang YAN Shuiguang DENG college of computer science and technology Zhejiang University college of computer science and technology Shanghai University 2012 Lab Huawei Technologies Co. Ltd.

Emerging applications such as smart city infrastructures and virtual reality landscapes are setting rigorous benchmarks for 6G mobile networks, requiring elevated levels of confidentiality, integrity, nonrepudiation, authentication, and stringent access controls. Blockchain technology is heralded as a transformative enabler for meeting 6G standards, owing to its intrinsic attributes. However, a gap exists in the holistic investigation of blockchain's applicability in 6G realms, particularly addressing the “whether”, “when”, and“how” of its deployment. Present research trails in developing robust methodologies to gauge blockchain's efficacy within 6G use cases. Addressing this, our study introduces a novel confluence of blockchain with 6G networks, where data resides in distributed Hash tables(DHTs) while their hashes are secured in distributed ledger technology(DLT), harnessing blockchain's core strengths-immutability, traceability, and fortified security. We delineate seven specific 6G use cases poised for enhancement through blockchain integration,and scrutinize the rationale, nature, and timing of this convergence. Furthermore, we devise a comprehensive methodology for assessing blockchain's performance metrics and scalability in 6G environments. Our extensive experimental analyses evaluate the synergistic performance of this integration, revealing that the Quorum blockchain satisfactorily supports 80% of 6G scenarios. The findings suggest that, with appropriate configurations, consortium blockchains are well-equipped to fulfill the demanding performance and scalability requisites of 6G networks.

关键词： blockchain distributed ledger technology (DLT) 6G performance assessment 6G scenarios analysis transaction arrival rate

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Large language model and domain-specific model collaboration for smart education

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Frontiers of Information technology & Electronic Engineering 2024年第3期25卷 333-341页

作者： Yawei LUO Yi YANG School of Software technology Zhejiang UniversityNingbo 315048China college of computer science and technology Zhejiang UniversityHangzhou 310027China

In this paper,we introduce the large language model and domain-specific model collaboration(LDMC)framework designed to enhance smart *** LDMC framework leverages the comprehensive and versatile knowledge of large domain-general models,combines it with the specialized and disciplinary knowledge from small domainspecific models(DSMs),and incorporates pedagogy knowledge from learning theory *** integration yields multiple knowledge representations,fostering personalized and adaptive educational *** explore various applications of the LDMC framework in the context of smart education.

关键词： smart language knowledge

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PointNetV3: feature extraction with position encoding

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Optoelectronics Letters 2024年第8期20卷 483-489页

作者： WANG Jun WANG Xuefei ZHOU Boxiong GUO Dongyan college of computer science and technology Zhejiang University of Technology

Feature extraction of point clouds is a fundamental component of three-dimensional(3D) vision tasks. While existing feature extraction networks primarily focus on enhancing the geometric perception abilities of networks and overlook the crucial role played by coordinates. For instance, though two airplane wings share the same shape, it demands distinct feature representations due to their differing positions. In this paper, we introduce a novel module called position aware module(PAM) to leverage the coordinate features of points for positional encoding, and integrating this encoding into the feature extraction network to provide essential positional context. Furthermore, we embed PAM into the Point Net++ framework, and design a novel feature extraction network, named Point Net V3. To validate the effectiveness of Point Net V3, we conducted comprehensive experiments including classification, object tracking and object detection on point cloud. The results of remarkable improvement in three tasks demonstrate the exceptional performance achieved by Point Net V3 in point cloud processing.

关键词： A

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A Robust Framework for Multimodal Sentiment Analysis with Noisy Labels Generated from Distributed Data Annotation

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computer Modeling in Engineering & sciences 2024年第6期139卷 2965-2984页

作者： Kai Jiang Bin Cao Jing Fan School of computer science and technology Zhejiang University of TechnologyHangzhou310023China

Multimodal sentiment analysis utilizes multimodal data such as text,facial expressions and voice to detect people’s *** the advent of distributed data collection and annotation,we can easily obtain and share such multimodal ***,due to professional discrepancies among annotators and lax quality control,noisy labels might be *** research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the *** address this challenging problem,we present a Multimodal Robust Meta Learning framework(MRML)for multimodal sentiment analysis to resist noisy labels and correlate distinct modalities ***,we propose a two-layer fusion net to deeply fuse different modalities and improve the quality of the multimodal data features for label correction and network ***,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy *** conducted experiments on three popular multimodal datasets to verify the superiority of ourmethod by comparing it with four baselines.

关键词： Distributed data collection multimodal sentiment analysis meta learning learn with noisy labels

PURP: A Scalable System for Predicting Short-Term Urban TrafficFlow Based on License Plate Recognition Data

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Big Data Mining and Analytics 2024年第1期7卷 171-187页

作者： Shan Zhang Qinkai Jiang Hao Li Bin Cao Jing Fan School of computer science and technology Zhejiang University of TechnologyHangzhou 310000China

Accurate and efficient urban traffic flow prediction can help drivers identify road traffic conditions in real-time,consequently helping them avoid congestion and accidents to a certain ***,the existing methods for real-time urban traffic flow prediction focus on improving the model prediction accuracy or efficiency while ignoring the training efficiency,which results in a prediction system that lacks the scalability to integrate real-time traffic flow into the training *** conduct accurate and real-time urban traffic flow prediction while considering the latest historical data and avoiding time-consuming online retraining,herein,we propose a scalable system for Predicting short-term URban traffic flow in real-time based on license Plate recognition data(PURP).First,to ensure prediction accuracy,PURP constructs the spatio-temporal contexts of traffic flow prediction from License Plate Recognition(LPR)data as effective ***,to utilize the recent data without retraining the model online,PURP uses the nonparametric method k-Nearest Neighbor(namely KNN)as the prediction framework because the KNN can efficiently identify the top-k most similar spatio-temporal contexts and make predictions based on these contexts without time-consuming model retraining *** experimental results show that PURP retains strong prediction efficiency as the prediction period increases.

关键词： traffic flow prediction k-Nearest Neighbor(KNN) License Plate Recognition(LPR)data spatio-temporalcontext

Towards understanding bogus traffic service in online social networks

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Frontiers of Information technology & Electronic Engineering 2024年第3期25卷 415-431页

作者： Ping HE Xuhong ZHANG Changting LIN Ting WANG Shouling JI college of computer science and technology Zhejiang UniversityHangzhou 310027China Binjiang Institute of zhejiang university Hangzhou 310027China college of Information sciences and technology Pennsylvania State UniversityUniversity Park 17057-4846USA

Critical functionality and huge infuence of the hot trend/topic page(HTP)in microblogging sites have driven the creation of a new kind of underground service called the bogus traffic service(BTS).BTS provides a kind of illegal service which hijacks the HTP by pushing the controlled topics into it for malicious customers with the goal of guiding public *** hijack HTP,the agents of BTS maintain an army of black-market accounts called bogus trafic accounts(BTAs)and control BTAs to generate a burst of fake trafic by massively retweeting the tweets containing the customer desired topic(hashtag).Although this service has been extensively exploited by malicious customers,little has been done to understand *** this paper,we conduct a systematic measurement study of the *** first investigate and collect 125 BTS agents from a variety of sources and set up a honey pot account to capture BTAs from these *** then build a BTA detector that detects 162218 BTAs from Weibo,the largest Chinese microblogging site,with a precision of 94.5%.We further use them as a bridge to uncover 296916 topics that might be involved in bogus ***,we uncover the operating mechanism from the perspectives of the attack cycle and the attack *** highlights of our findings include the temporal attack patterns and intelligent evasion tactics of the *** findings bring BTS into the *** work will help in understanding and ultimately eliminating this threat.

关键词： Online social networks Measurement Bogus traffic Black market

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Small object detection on highways via balance feature fusion and task-specific encoding network

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Optoelectronics Letters 2024年第7期20卷 424-429页

作者： YU Minming CHAN Sixian ZHOU Xiaolong LAI Zhounian college of computer science and technology Zhejiang University of Technology Hangzhou Xsuan technology Co. Ltd. Quzhou university Huzhou Institute of zhejiang university

Detecting small objects on highways is a novel research topic. Due to the small pixel of objects on highways, traditional detectors have difficulty in capturing discriminative features. Additionally, the imbalance of feature fusion methods and the inconsistency between classification and regression tasks lead to poor detection performance on highways. In this paper, we propose a balance feature fusion and task-specific encoding network to address these issues. Specifically, we design a balance feature pyramid network(FPN) to integrate the importance of each layer of feature maps and construct long-range dependencies among them, thereby making the features more discriminative. In addition, we present task-specific decoupled head, which utilizes task-specific encoding to moderate the imbalance between the classification and regression tasks. As demonstrated by extensive experiments and visualizations, our method obtains outstanding detection performance on small object detection on highways(HSOD) dataset and AI-TOD dataset.

关键词： A

Deep3DSketch-im:rapid high-fidelity AI 3D model generation by single freehand sketches

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Frontiers of Information technology & Electronic Engineering 2024年第1期25卷 149-159页

作者： Tianrun CHEN Runlong CAO Zejian LI Ying ZANG Lingyun SUN college of computer science and technology Zhejiang UniversityHangzhou 310027China School of Software technology Zhejiang UniversityHangzhou 310027China School of Information Engineering Huzhou UniversityHuzhou 313000China

The rise of artificial intelligence generated content(AIGC)has been remarkable in the language and image fields,but artificial intelligence(AI)generated three-dimensional(3D)models are still under-explored due to their complex nature and lack of training *** conventional approach of creating 3D content through computer-aided design(CAD)is labor-intensive and requires expertise,making it challenging for novice *** address this issue,we propose a sketch-based 3D modeling approach,Deep3DSketch-im,which uses a single freehand sketch for *** is a challenging task due to the sparsity and ***3DSketch-im uses a novel data representation called the signed distance field(SDF)to improve the sketch-to-3D model process by incorporating an implicit continuous field instead of voxel or points,and a specially designed neural network that can capture point and local *** experiments are conducted to demonstrate the effectiveness of the approach,achieving state-of-the-art(SOTA)performance on both synthetic and real ***,users show more satisfaction with results generated by Deep3DSketch-im,as reported in a user *** believe that Deep3DSketch-im has the potential to revolutionize the process of 3D modeling by providing an intuitive and easy-to-use solution for novice users.

关键词： Content creation Sketch Three-dimensional(3D)modeling 3D reconstruction Shape from X Artificial intelligence(AI)

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CTSN: Predicting cloth deformation for skeleton-based characters with a two-stream skinning network

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Computational Visual Media 2024年第3期10卷 471-485页

作者： Yudi Li Min Tang Yun Yang Ruofeng Tong Shuangcai Yang Yao Li Bailin An Qilong Kou college of computer science and technology Zhejiang UniversityHangzhou 310058China Aurora Studios TencentShenzhen 518057China

We present a novel learning method using a two-stream network to predict cloth deformation for skeleton-based *** characters processed in our approach are not limited to humans,and can be other targets with skeleton-based representations such asﬁsh or *** use a novel network architecture which consists of skeleton-based and mesh-based residual networks to learn the coarse features and wrinkle features forming the overall residual from the template cloth *** network may be used to predict the deformation for loose or tight-fitting *** memory footprint of our network is low,thereby resulting in reduced computational *** practice,a prediction for a single cloth mesh for a skeleton-based character takes about 7 ms on an nVidia GeForce RTX 3090 *** to prior methods,our network can generate finer deformation results with details and wrinkles.

关键词： cloth deformation learning network skinning