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A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

Frontiers of Earth Science 2021年第3期15卷 620-630页

作者： Menghan ZHANG Mingjun MA Jingying ZHANG Mingzhuo ZHANG Bo LIW Dehui DU School of Software and Engineering East China Normal UniversityShanghai 200062China Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China MOE International Joint Lab of Trustworthy Software East China Normal UniversityShanghai 200062China

Nowadays,autonomous driving has been attracted widespread attention from academia and *** we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep ***,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep *** novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous *** approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD *** further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is *** results show that our approach facilitates the training process of AI components with the STTD.

关键词： spatio-temporal trajectory data data metamodeling domain knowledge LSTM vehicle behavior prediction AI component

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A Robust and Generalized Gauss-Seidel Solver for Physically-Correct Simultaneous Collisions

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Proceedings of the ACM on Computer Graphics and Interactive Techniques 2025年第1期8卷 1-17页

作者： Huanbo Zhou Zhenyu Xu Xijun Liu Xinyu Zhang Software Engineering Institute East China Normal University Shanghai Shanghai China East China Normal University Shanghai Shanghai China Software Engineering Institute East China Normal University Shanghai Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai Shanghai China and Engineering Research Center of Software/Hardware Co-design Technology and Application MoE) Shanghai Shanghai China

Simulating multi-object collisions in real-time environments remains a significant challenge, particularly when handling simultaneous collisions in a physically accurate manner. Traditional Gauss-Seidel solvers, widely employed in physics engines, often fail to preserve the symmetry and consistency of multi-object interactions that are often observed in physics. In this paper, we present a generalized and robust Gauss-Seidel solver to overcome the difficulties in simultaneous collisions. Using spatial and temporal collision states to classify and resolve constraints, our algorithm ensures correct collision propagation and symmetry, addressing the limitations commonly found in existing solvers. Moreover, our algorithm can mitigate jitters caused by floating-point errors, ensuring smooth and stable collision responses. Our approach demonstrates fast convergence and improved accuracy in scenarios involving complex multi-object collisions.

关键词： Physically-based simulation Gauss-Seidel solver Simultaneous collisions

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A Novel and Pragmatic Scenario Modeling Framework with Verification-in-the-loop for Autonomous Driving Systems

A Novel and Pragmatic Scenario Modeling Framework with Verif...

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IEEE/ACM International Conference on software engineering: New Ideas and Emerging Technologies Results Track (ICSE-NIER)

作者： Dehui Du Bo Li Chenghang Zheng Xinyuan Zhang Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai China

Scenario modeling for Autonomous Driving Systems (ADS) enables scenario-based simulation and verification which are critical for the development of safe ADS. However, with the increasing complexity and uncertainty of ADS, it becomes increasingly challenging to manually model driving scenarios and conduct verification analysis. To tackle these challenges, we propose a novel and pragmatic framework for scenario modeling, simulation and verification. The novelty is that it’s a verification-in-the-loop scenario modeling framework. The scenario modeling language with formal semantics is proposed based on the domain knowledge of ADS. It facilitates scenario verification to analyze the safety of scenario models. Moreover, the scenario simulation is implemented based on the scenario executor. Compared with existing works, our framework can simplify the description of scenarios in a non-programming, user-friendly manner, model stochastic behavior of vehicles, support safe verification of scenario models with UPPAAL-SMC and generate executable scenario in some open-source simulators such as CARLA. To preliminarily demonstrate the effectiveness and feasibility of our approach, we build a prototype tool and apply our approach in several typical scenarios for ADS.

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Think Before You Act: A Two-Stage Framework for Mitigating Gender Bias Towards Vision-Language Tasks

arXiv

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arXiv 2024年

作者： Zhang, Yunqi Li, Songda Deng, Chunyuan Wang, Luyi Zhao, Hui Software Engineering Institute East China Normal University China Georgia Institute of Technology United States Shanghai Key Laboratory of Trustworthy Computing Shanghai China

Gender bias in vision-language models (VLMs) can reinforce harmful stereotypes and discrimination. In this paper, we focus on mitigating gender bias towards vision-language tasks. We identify object hallucination as the essence of gender bias in VLMs. Existing VLMs tend to focus on salient or familiar attributes in images but ignore contextualized nuances. Moreover, most VLMs rely on the co-occurrence between specific objects and gender attributes to infer the ignored features, ultimately resulting in gender bias. We propose GAMA, a task-agnostic generation framework to mitigate gender bias. GAMA consists of two stages: narrative generation and answer inference. During narrative generation, GAMA yields all-sided but gender-obfuscated narratives, which prevents premature concentration on localized image features, especially gender attributes. During answer inference, GAMA integrates the image, generated narrative, and a task-specific question prompt to infer answers for different vision-language tasks. This approach allows the model to rethink gender attributes and answers. We conduct extensive experiments on GAMA, demonstrating its debiasing and generalization ability. © 2024, CC BY-NC-ND.

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MPC-Based Privacy-Preserving Serverless Federated Learning 3

MPC-Based Privacy-Preserving Serverless Federated Learning

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3rd International Conference on Big Data, Artificial Intelligence and Internet of Things engineering, ICBAIE 2022

作者： Zhong, Liangyu Zhang, Lei Xu, Lin Wang, Lulu East China Normal University Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan Chengdu610041 China

ISBN: (数字)9781665451604

ISBN: (纸本)9781665451604

Federated learning (FL) enables multiple users to collaboratively train a global model by keeping their data sets local. Since a single server is used, traditional FL faces the single point of failure problem. An available approach is to adopt a serverless architecture. However, most of existing serverless FL schemes fail to protect gradient privacy, except for a few schemes that adopt differential privacy (DP) where the global model accuracy will decrease. To address these problems, we propose a privacy-preserving serverless FL scheme based on secure multiparty computation (MPC). Combining multiple cryptographic primitives (e.g., key agreement and symmetric encryption), our scheme protects gradient privacy in FL and it is accuracy-lossless. By secret sharing, our scheme supports users to quit an FL task in each round during training. © 2022 IEEE.

关键词： Privacy-preserving techniques

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Error Classification of Large Language Models on Math Word Problems: A Dynamically Adaptive Framework

arXiv

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arXiv 2025年

作者： Sun, Yuhong Yin, Zhangyue Huang, Xuanjing Qiu, Xipeng Zhao, Hui Software Engineering Institute East China Normal University China School of Computer Science Fudan University China Shanghai Key Laboratory of Trustworthy Computing Shanghai China

Large Language Models (LLMs) have demonstrated remarkable capabilities across various domains. Math Word Problems (MWPs) serve as a crucial benchmark for evaluating LLMs’ reasoning abilities. While most research primarily focuses on improving accuracy, it often neglects understanding and addressing the underlying patterns of errors. Current error classification methods rely on static and predefined categories, which limit their ability to capture the full spectrum of error patterns in mathematical reasoning. To enable systematic error analysis, we collect error samples from 15 different LLMs of varying sizes across four distinct MWP datasets using multiple sampling strategies. Based on this extensive collection, we introduce MWPES-300K, a comprehensive dataset containing 304,865 error samples that cover diverse error patterns and reasoning paths. To reduce human bias and enable fine-grained analysis of error patterns, we propose a novel framework for automated dynamic error classification in mathematical reasoning. Experimental results demonstrate that dataset characteristics significantly shape error patterns, which evolve from basic to complex manifestations as model capabilities increase. With deeper insights into error patterns, we propose error-aware prompting that incorporates common error patterns as explicit guidance, leading to significant improvements in mathematical reasoning performance. Copyright © 2025, The Authors. All rights reserved.

关键词： Systematic errors

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Asymmetric Group key Agreement Protocol from Short Signatures 8

Asymmetric Group Key Agreement Protocol from Short Signature...

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8th IEEE International Conference on Computer and Communications, ICCC 2022

作者： Shen, Kunyu Zhang, Lei Zhang, Rui Fang, Qihao Shanghai Key Laboratory of Trustworthy Computing Software Engineering Institute East China Normal University Shanghai200062 China Science and Technology on Communication Security Laboratory Sichuan Chengdu610041 China

ISBN: (纸本)9781665450515

Asymmetric group key agreement allows a group of users to negotiate a public encryption key that corresponds to several decryption keys, and each decryption key can only be computed by one group member. This novel notion ensures the confidentiality of communication among group members and allows any external entity to send messages to the group. However, the existing authenticated asymmetric group key agreement protocols are designed in identity-based cryptosystem or certificateless public key cryptosystem, which are not widely deployed. In this paper, we propose an efficient authenticated asymmetric group key agreement protocol. The protocol captures the security of secrecy, known-key security, key-compromise impersonation, unknown key-share and key control while being resistant to active attacks. The security of our protocol is reduced to the k-BDHE problem. © 2022 IEEE.

关键词： Public key cryptography

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Generic 2-Party PFE with Constant Rounds and Linear Active Security, and Efficient Instantiation 1

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18th EAI International Conference on Security and Privacy in Communication Networks, SecureComm 2022

作者： Jia, Hanyu Li, Xiangxue Li, Qiang Bao, Yue Hou, Xintian School of Software Engineering East China Normal University Shanghai200062 China Shanghai Key Laboratory of Privacy-Preserving Computation MatrixElements Technologies Shanghai201204 China Shanghai Key Laboratory of Trustworthy Computing Shanghai200062 China Institute of Cyber Science and Technology Shanghai Jiaotong University Shanghai200240 China Co. Ltd. Tianjin300300 China

ISBN: (数字)9783031255380

ISBN: (纸本)9783031255373

The paper considers generic construction of 2-party private function evaluation (PFE) in the malicious adversary model. There is hitherto only one concrete design of actively secure 2-party PFE protocol (Liu et al. at PKC 2022, and LWY hereafter) with constant rounds and linear complexity. One interesting feature of LWY is its function reusability (i.e., the same function is involved in multiple executions of LWY) which makes its execution more efficiently from the second execution. Nevertheless, in its first execution (in particular for those settings where only one invocation of the function is required), LWY is quite involved and too inefficient to be of practical use. For these settings (of non-reusable private functions), we initiate a generic construction of 2-party PFE protocol with constant rounds and linear complexity in the malicious adversary model based on Yao’s garbled circuit and singly homomorphic encryption. When instantiated with ElGamal encryption and Groth secret shuffle (J. Cryptology 2010), the generic construction effectuates a novel concrete design of 2-party PFE, which has better performance and reduces 51.2% communication bits and 52.4% computation costs, compared to LWY (in its first execution) at the same security level. It even outperforms several 2-party PFE protocols (Katz and Malka at AISACRYPT 2011, and Mohassel and Sadeghian at EUROCRYPT 2013) that are secure in the semi-honest adversary model from the communication perspective. The proposed PFE and LWY thus make optimal solutions available for non-reusable and reusable private functions, respectively. © 2023, ICST institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Function evaluation

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Effective Entry-Wise Flow for Molecule Generation 40

Effective Entry-Wise Flow for Molecule Generation

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40th IEEE International Conference on Data engineering, ICDE 2024

作者： Zhang, Qifan Yao, Junjie Yang, Yuquan Shi, Yizhou Gao, Wei Wang, Xiaoling School of Computer Science and Technology East China Normal University China Software Engineering Institute East China Normal University China Shanghai Key Laboratory of Trustworthy Computing China University of Massachusetts Department of Chemistry Boston United States University of Michigan Ann Arbor United States

ISBN: (纸本)9798350317152

Molecule generation is a critical process in the fields of drug discovery and materials science. Recently, generative models based on normalizing flows have demonstrated significant potential in this domain. These models are particularly suited for handling the symmetrical and complex chemical structures often encountered in molecular datasets. Despite their promising nature, normalizing flow-based models for molecule generation face considerable challenges. The complexity of molecule representation, the rigorous demands of optimization, and the scarcity of training labels in molecular datasets contribute to these difficulties. Additionally, adequately and comprehensively learning the distribution of molecular datasets remains a formidable task. In this paper, we delve into the intricate entry-wise modules in vanilla flows, introducing an effective variation of flow-based models. Our proposed approach innovatively encapsulates affine coupling transformations within normalizing flows. Furthermore, we deconstruct existing invertible flow models, integrating them with newly developed entry-wise transformations. Our experimental studies demonstrate that these proposed entry-wise modules, when incorporated into standard flow-based models, surpass other generative models in performance on various representative datasets and generation tasks. Notably, in the context of low-resourced molecular graph generation, our model achieves remarkable performance compared to its counterparts. © 2024 IEEE.

关键词： Complex networks

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RSED: Zero-Shot Relation Triplet Extraction via Relation Selection and Entity Boundary Detection

RSED: Zero-Shot Relation Triplet Extraction via Relation Sel...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Yuquan Lan Dongxu Li Yunqi Zhang Hui Zhao Gang Zhao Software Engineering Institute East China Normal University Shanghai China Shanghai Key Laboratory of Trustworthy Computing Shanghai China China Resources Pharma Comm Beijing China

Zero-shot relation triplet extraction (ZeroRTE) aims to extract relation triplets of unseen relation types from unstructured texts, with a core challenge of training models to recognize new relations without labeled data. The seminal work handles this task by leveraging pre-trained language models to generate synthetic data for new relations, which requires a laborious training procedure. Moreover, the quality of the synthetic data significantly influences the performance. We propose a model named RSED to tackle the ZeroRTE task via Relation Selection and Entity boundary Detection, which considerably simplifies the procedure and is agnostic to the synthetic data. The whole model is composed of three modules. The first relation selection module utilizes a novel relation selection modeling approach to enable the recognition of unseen relations. Such a modelling approach breaks free from the constraints of specific relation types, naturally infuses the relation semantics, and eliminates the need for synthetic data. The second relation filtering module removes irrelevant relations. The third entity boundary detection module extracts multiple entities following a set prediction paradigm. We evaluate our model on two ZeroRTE datasets. The experiment results show that our method consistently outperforms previous works.

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