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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 extent.However,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 procedure.To 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 characteristics.Subsequently,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 online.The 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

Multistage guidance on the diffusion model inspired by human artists’ creative thinking

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Frontiers of Information Technology & Electronic Engineering 2024年第1期25卷 170-178页

作者： Wang QI Huanghuang DENG Taihao LI AI Research Institute Zhejiang LabHangzhou 311121China department of computer science and Technology Zhejiang UniversityHangzhou 310027China

Current research on text-conditional image generation shows parallel performance with ordinary painters but still has much room for improvement when compared to that of artist-ability paintings,which usually represent multilevel semantics by gathering features ofmultiple objects into one object.In a preliminary experiment,we confirm this and then seek the opinions of three groups of individuals with varying levels of art appreciation ability to determine the distinctions that exist between painters and artists.We then use these opinions to improve an artificial intelligence(AI)painting system from painter-level image generation toward artistic-level image generation.

关键词： gathering ordinary artist

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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 education.The 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 models.This integration yields multiple knowledge representations,fostering personalized and adaptive educational experiences.We explore various applications of the LDMC framework in the context of smart education.

关键词： smart language knowledge

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SwiftTheft: A Time-Efficient Model Extraction Attack Framework Against Cloud-Based Deep Neural Networks

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Chinese Journal of Electronics 2024年第1期33卷 90-100页

作者： Wenbin YANG Xueluan GONG Yanjiao CHEN Qian WANG Jianshuo DONG School of Cyber science and Engineering Wuhan University School of computer science Wuhan University College of Electrical Engineering Zhejiang University

With the rise of artificial intelligence and cloud computing, machine-learning-as-a-service platforms,such as Google, Amazon, and IBM, have emerged to provide sophisticated tasks for cloud applications. These proprietary models are vulnerable to model extraction attacks due to their commercial value. In this paper, we propose a time-efficient model extraction attack framework called Swift Theft that aims to steal the functionality of cloud-based deep neural network models. We distinguish Swift Theft from the existing works with a novel distribution estimation algorithm and reference model settings, finding the most informative query samples without querying the victim model. The selected query samples can be applied to various cloud models with a one-time selection. We evaluate our proposed method through extensive experiments on three victim models and six datasets, with up to 16 models for each dataset. Compared to the existing attacks, Swift Theft increases agreement(i.e., similarity) by 8% while consuming 98% less selecting time.

关键词： Artificial intelligence security Model extraction attacks Deep neural networks

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

What can we learn from quality assurance badges in open-source software?

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science China(Information sciences) 2024年第4期67卷 146-163页

作者： Feng LI Yiling LOU Xin TAN Zhenpeng CHEN Jinhao DONG Yang LI Xuanzhi WANG Dan HAO Lu ZHANG MoE Key Lab of HCST School of Computer SciencePeking University department of computer science Purdue University School of computer science & Engineering Beihang University(BUAA) department of computer science University College London(UCL)

In the development of open-source software(OSS), many developers use badges to give an overview of the software and share some key features/metrics conveniently. Among various badges, quality assurance(QA) badges make up a large proportion and are the most prevalent because QA is of vital importance in software development, and ineffective QA may lead to anomalies or defects. In this paper, we focus on QA badges in open-source projects, which present quality assurance information directly and instantly,and aim to produce some interesting findings and provide practical implications. We collect and analyze 100000 projects written in popular programming languages from GitHub and conduct a comprehensive empirical study both inside and outside QA badges. Inside QA badges, we build a category classification for all QA badges based on the properties they focus on, which shows the types of QA badges developers use. Then,we analyze the frequency of the properties that QA badges focus on, and property combinations, too, which present their use status. We find that QA badges focus on various properties while developers give different preferences to different properties. The use status also differs between different programming languages. For example, projects written in C focus on Security to a great extent. Our findings also provide implications for developers and badge providers. Outside QA badges, we conduct a correlation analysis between QA badges and some software metrics that have potential relationships with code quality, contribution quality, and popularity. We find that QA badges have statistically significant correlations with various software metrics.

关键词： quality assurance badge open-source software code quality empirical study

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 attitudes.With the advent of distributed data collection and annotation,we can easily obtain and share such multimodal data.However,due to professional discrepancies among annotators and lax quality control,noisy labels might be introduced.Recent research suggests that deep neural networks(DNNs)will overfit noisy labels,leading to the poor performance of the DNNs.To 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 simultaneously.Specifically,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 training.Besides,a multiple meta-learner(label corrector)strategy is proposed to enhance the label correction approach and prevent models from overfitting to noisy labels.We 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

RGB oralscan video-based orthodontic treatment monitoring

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science China(Information sciences) 2024年第1期67卷 124-139页

作者： Yan TIAN Hanshi FU Hao WANG Yuqi LIU Zhaocheng XU Hong CHEN Jianyuan LI Ruili WANG School of computer science and Technology Zhejiang Gongshang University Shining3D Tech Co. Ltd. School of Information and Technology Monash University School of Mathematical and Computational sciences Massey University department of Stomatology Zhejiang Provincial People's HospitalHangzhou Medical College School of computer and Computing science Zhejiang University City College

Orthodontic treatment monitoring involves using current images and previous 3D models to estimate the relative position of individual teeth before and after orthodontic treatment.This process differs from image-based object 6D pose estimation due to the gingiva deformation and varying pose offsets for each tooth during treatment.Motivated by the fact that the poses of molars remain relatively fixed in implicit orthodontics,we design an approach that employs multiview pose evaluation and bidirectional temporal propagation for jaw pose estimation and then employs an iteration-based method for tooth alignment.To handle changes in tooth appearance or location with weak texture across frames,we also introduce an instance propagation module that leverages positional and semantic information to explore instance relations in the temporal domain.We evaluated the performance of our approach using both the Shining3D tooth pose dataset and the Aoralscan3 tooth registration dataset.Our experimental results demonstrate remarkable accuracy improvements compared with existing methods.

关键词： digital dentistry object 6D pose estimation deep learning computer vision

Control of defensive behavior by the nucleus of Darkschewitsch GABAergic neurons

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National science Review 2024年第4期11卷 70-83页

作者： Huiying Zhao Jinrong Liu Yujin Shao Xiang Feng Binhan Zhao Li Sun Yijun Liu Linghui Zeng Xiao-ming Li Hongbin Yang Shumin Duan Yan-qin Yu department of Neurology of Second Affiliated Hospital and School of Brain science and Brain Medicine Zhejiang University School of Medicine Nanhu Brain-computer Interface Institute Liangzhu Laboratory MOE Frontier Science Center for Brain Science and Brain-Machine Integration State Key Laboratory of Brain-Machine Intelligence Zhejiang University NHC and CAMS Key Laboratory of Medical Neurobiology Zhejiang University Key Laboratory of Novel Targets and Drug Study for Neural Repair of zhejiang Province School of Medicine Hangzhou City University

The nucleus of Darkschewitsch(ND), mainly composed of GABAergic neurons, is widely recognized as a component of the eye-movement controlling system. However, the functional contribution of ND GABAergic neurons(NDGABA) in animal behavior is largely unknown. Here, we show that NDGABA neurons were selectively activated by different types of fear stimuli, such as predator odor and foot shock.Optogenetic and chemogenetic manipulations revealed that NDGABA neurons mediate freezing behavior.Moreover, using circuit-based optogenetic and neuroanatomical tracing methods, we identified an excitatory pathway from the lateral periaqueductal gray(l PAG) to the ND that induces freezing by exciting ND inhibitory outputs to the motor-related gigantocellular reticular nucleus, ventral part(Gi V). Together,these findings indicate the NDGABA population as a novel hub for controlling defensive response by relaying fearful information from the l PAG to Gi V, a mechanism critical for understanding how the freezing behavior is encoded in the mammalian brain.

关键词： nucleus of Darkschewitsch GABAergic population defensive behavior freezing neural circuitry