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5th International Seminar on Artificial Intelligence, Networking and Information technology, AINIT 2024

作者： Wu, Jianhua Zhang, Xin Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai201112 China

ISBN: (纸本)9798350385557

As an important information system of intelligent and connected vehicles, most In-Vehicle Infotainment systems are set up and manage the vehicles through user interaction. This paper presents a Fuzzing method IVIFUZZER for vulnerability exploitation on the operation interface of the In-Vehicle Infotainment system. With the help of automated robotic arm and improved OCR technology, collecting system leakage information and directional detection of the identified input points, 6 zero day vulnerabilities were found. The experimental results show that the proposed IVIFUZZER method is effective. © 2024 IEEE.

关键词： Vehicles

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Survey on Testing of Autonomous Driving Systems 3

Survey on Testing of Autonomous Driving Systems

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Chen, Wenjie Ma, Zeyu Wang, Chao Chen, Mingang Cai, Lizhi Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

Before autonomous driving vehicles are commercialized, they need to undergo a series of rigorous tests. This paper first proposes the general process of autonomous driving system testing, and then summarizes the research progress of autonomous driving vehicle testing from the model, simulation, network, and vehicle levels, and analyzes the characteristics of various testing technologies. Finally, this paper gives suggestions on the development of autonomous driving testing technology. © 2021 for this paper by its authors

关键词： Autonomous vehicles

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Research on Quality Model and Measurement for Microservices 3

Research on Quality Model and Measurement for Microservices

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3rd International Workshop on Experience with SQuaRE Series and Its Future Direction, IWESQ 2021

作者： Yu, Jinchuan Ge, Jianxin Sun, Jixin Software Engineering Institute Shanghai Key Laboratory of Computer Software Testing & Evaluation Shanghai Development Center of Computer Software Technology Shanghai China

In recent years, microservice architecture (MSA) has become popular. Emerging from the agile community, MSA implies a number of small, independently deployable microservices. They are characterized by low coupling, high cohesion, low complexity, and are more flexible and convenient in application, saving resource ***, there is limited research on quality models for MSA. Although MSA is a special form of service-oriented architecture, there are still some differences between the two that are hard to ignore, such as decentralization, smaller service size and encouraging technical heterogeneity. Therefore, it is difficult to directly apply the traditional quality model research of service-oriented architecture to MSA. Based on the above considerations, this paper proposes a quality model for MSA, which reflects the quality characteristics of microservices through 16 measures for each of the Functional Suitability, Flexibility, Interactivity, Performance Efficiency, Reliability and Security. At the same time, to address the shortage of theoretical validation of current research, this paper uses static analysis and dynamic analysis to validate the proposed measures and prove the rationality of its theory. © 2021 for this paper by its authors

关键词： Static analysis

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Hybrid Intelligent Approach for the Selection of Third-Party Reverse Logistics Provider under Uncertainty

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Journal of Donghua University(English Edition) 2014年第4期31卷 484-492页

作者：宫艳雪宋俊典彭亦功添玉郑树泉 Intelligent Product Innovation Center Shanghai Industrial Technology Institute Shanghai Development Center of Computer Software Technology School of Information East China University of Science and Technology College of Logistics Engineering Shanghai Maritime University

A hybrid intelligent approach is proposed to help the decision maker to select the appropriate third-party reverse logistics provider. The following process is included: firstly,the evaluation team is established to determine the selection criteria and evaluate them by triangular fuzzy numbers; secondly,calculate the weight of criteria by the proposed hybrid algorithm integrating particle swarm optimization( PSO) and simulated annealing( SA); then, the performance evaluation for each supplier is predicted by the proposed self-feedback neural network( SFBNN) based on the historical data. A numerical example is also presented to interpret the methodology above.

关键词： hybrid intelligent approach third-party reverse logistics provider uncertainty

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H2-MARL: Multi-Agent Reinforcement Learning for Pareto Optimality in Hospital Capacity Strain and Human Mobility during Epidemic

arXiv

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arXiv 2025年

作者： Luo, Xueting Deng, Hao Yang, Jihong Shen, Yao Guo, Huanhuan Sun, Zhiyuan Liu, Mingqing Wei, Jiming Zhao, Shengjie School of Computer Science and Technology Tongji University Shanghai201804 China China Land Surveying and Planning Institute Beijing100035 China College of Architecture and Urban Planning Tongji University Shanghai200092 China Chongqing Institute of Planning and Natural Resources Investigation and Monitoring Chongqing401121 China Li-Fi Research and Development Centre University of Cambridge CambridgeCB3 0FA United Kingdom Guangdong Urban-rural Planning and Design Research Institute Technology Group Co. Ltd. Guangzhou510290 China Engineering Research Center of Key Software Technologies for Smart City Perception and Planning Ministry of Education Shanghai201804 China

The necessity of achieving an effective balance between minimizing the losses associated with restricting human mobility and ensuring hospital capacity has gained significant attention in the aftermath of COVID-19. Reinforcement learning (RL)-based strategies for human mobility management have recently advanced in addressing the dynamic evolution of cities and epidemics;however, they still face challenges in achieving coordinated control at the township level and adapting to cities of varying scales. To address the above issues, we propose a multi-agent RL approach that achieves Pareto optimality in managing hospital capacity and human mobility (H2-MARL), applicable across cities of different scales. We first develop a township-level infection model with online-updatable parameters to simulate disease transmission and construct a city-wide dynamic spatiotemporal epidemic simulator. On this basis, H2-MARL is designed to treat each division as an agent, with a trade-off dual-objective reward function formulated and an experience replay buffer enriched with expert knowledge built. To evaluate the effectiveness of the model, we construct a township-level human mobility dataset containing over one billion records from four representative cities of varying scales. Extensive experiments demonstrate that H2-MARL has the optimal dual-objective trade-off capability, which can minimize hospital capacity strain while minimizing human mobility restriction loss. Meanwhile, the applicability of the proposed model to epidemic control in cities of varying scales is verified, which showcases its feasibility and versatility in practical applications. Copyright © 2025, The Authors. All rights reserved.

关键词： Reinforcement learning

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Multi-View Self-Supervised Learning Enhances Automatic Sleep Staging from EEG Signals

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IEEE Transactions on Biomedical Engineering 2025年 PP卷 PP页

作者： Yu, Tianyou Hu, Xinxin He, Yanbin Wu, Wei Gu, Zhenghui Yu, Zhuliang Li, Yuanqing Wang, Fei Xiao, Jun South China University of Technology School of Automation Science and Engineering Guangzhou510640 China Pazhou Laboratory Research Center for Brain-Computer Interface Guangzhou510330 China Guangdong Work Injury Rehabilitation Hospital Department of Traumatic Brain Injury Rehabilitation and Severe Rehabilitation Guangzhou510440 China Shanghai Jiao Tong University School of Medicine Songjiang Hospital Songjiang Research Institute Shanghai Key Laboratory of Emotions and Affective Disorders Shanghai201600 China South China Normal University School of Software Guangzhou510631 China South China University of Technology School of Electric Power Engineering Guangzhou510640 China

Deep learning-based methods for automatic sleep staging offer an efficient and objective alternative to costly manual scoring. However, their reliance on extensive labeled datasets and the challenge of generalization to new subjects and datasets limit their widespread adoption. Self-supervised learning (SSL) has emerged as a promising solution to address these issues by learning transferable representations from unlabeled data. This study highlights the effectiveness of SSL in automated sleep staging, utilizing a customized SSL approach to train a multi-view sleep staging model. This model includes a temporal view feature encoder for raw EEG signals and a spectral view feature encoder for time-frequency features. During pretraining, we incorporate a cross-view contrastive loss in addition to a contrastive loss for each view to learn complementary features and ensure consistency between views, enhancing the transferability and robustness of learned features. A dynamic weighting algorithm balances the learning speed of different loss components. Subsequently, these feature encoders, combined with a sequence encoder and a linear classifier, enable sleep staging after finetuning with labeled data. Evaluation on three publicly available datasets demonstrates that finetuning the entire SSL-pretrained model achieves competitive accuracy with state-of-the-art methods—86.4%, 83.8%, and 85.5% on SleepEDF-20, SleepEDF-78, and MASS datasets, respectively. Notably, our framework achieves near-equivalent performance with only 5% of the labeled data compared to full-label supervised training, showcasing SSL's potential to enhance automated sleep staging efficiency. © 1964-2012 IEEE.

关键词： Self-supervised learning

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Cross-modality Complementary Interaction Network 8

Cross-modality Complementary Interaction Network

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8th International Conference on Intelligent Informatics and Biomedical Sciences, ICIIBMS 2023

作者： Cao, Zhengnan Gao, Yue Shanghai Institute of Technology School of Science Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350369564

Salient Object Detection (SOD) aims to identify the most conspicuous objects and regions in the human vision. This paper presents the Cross-Modality Complementary Interaction Network(CCINet), a novel approach for obtaining complementary features between different modalities. We introduce feature complementarity Module (FCM) to capture inter-modality complementary features. Subsequently, the Multi-Scale Aggregation Module (MAM) is utilized to acquire multilevel information across different resolutions. Finally, we employ a decoder to generate saliency maps. Experimental results demonstrate the superiority of our model over previous methods, as it outperforms them across four evaluation indicators on five challenging datasets. © 2023 IEEE.

关键词： Object detection

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A test case suite generation framework of scenario testing

A test case suite generation framework of scenario testing

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3rd International Conference on Advances in System Testing and Validation Lifecycle, VALID 2011

作者： Li, Ting Liu, Zhenyu Jiang, Xu Shanghai Development Center of Computer Software Technology Shanghai China Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China Shanghai Software Industry Association Shanghai China

ISBN: (纸本)9781612081687

This paper studies the software scenario testing, which is commonly used in black-box testing. In the paper, the workflow model based on task-driven, which is very common in scenario testing, is analyzed. According to test business model in scenario testing, the model is designed to corresponding test case suite. The test case suite that conforms to the scenario test can be obtained through test case generation and test item design. In the last part of the paper, framework of test case suite design is given to illustrate the effectiveness of the method.

关键词： Black-box testing

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Evaluation of Hallucination and Robustness for Large Language Models 23

Evaluation of Hallucination and Robustness for Large Languag...

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23rd IEEE International Conference on software Quality, Reliability, and Security Companion, QRS-C 2023

作者： Hu, Rui Zhong, Junhao Ding, Minjie Ma, Zeyu Chen, Mingang Shanghai Development Center of Computer Software Technology Shanghai Key Laboratory of Computer Software Testing and Evaluating Shanghai China Shanghai AI Industry Association Shanghai China

ISBN: (纸本)9798350359398

As large language models (LLMs) rapidly advance, rigorous testing and evaluation of these models grows increasingly crucial. To address this need, we have developed three types of questions: Chinese contextual, English contextual, and language context-independent. Testing in both Chinese and English probes the LLMs' hallucination tendencies. We investigate the impact of language on hallucinations from two perspectives: The type of language used in the input prompt and the cultural context underlying the prompt's content. Additionally, 52 multi-domain single-choice questions from C-EVAL are presented in original and randomized order to assess robustness to perturbations. Among the five LLMs, the tests demonstrate GPT-4 has the strongest anti-hallucination and robustness capabilities, answering with greater accuracy, consistency, and reliability. ChatGLM ranks second and outperforms GPT-4 on Chinese context-dependent questions. Emergent testing phenomena are analyzed from the user's perspective. Hallucinated responses are categorized and potential causal factors leading to hallucination and fragility are examined. Based on these findings, viable avenues for improvement are proposed. © 2023 IEEE.

关键词： Computational linguistics

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RSChat:Intelligent Question Answering Model for Railway Safety Knowledge 24

RSChat:Intelligent Question Answering Model for Railway Safe...

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24th IEEE/ACIS International Conference on computer and Information Science, ICIS 2024

作者： Li, Jinyu Li, Chao Niu, Sen Dai, Bingrong School of Computer and Information Engineering Shanghai Polytechnic University Shanghai China Shanghai Development Center of Computer Software Technology Shanghai China

ISBN: (纸本)9798350376647

The release of ChatGPT heralds a new era in artificial intelligence, as large language models (LLMs) empower myriad industries. This paper introduces RSChat, a model based on supervised fine-tuning of multiple large language models (LLMs), exploring a novel paradigm for applying knowledge in railway safety. Initially, we perform prompt engineering using collected domain data, including key information extraction and knowledge expansion. Subsequently, leveraging the constructed fine-tuning datasets, we conduct supervised fine-tuning using multiple large language models (LLMs) as base models. Finally, experimental evaluations demonstrate that RSChat can generate more professional responses in the railway safety domain, exhibiting robust potential for application. © 2024 IEEE.

关键词： Railroad accidents

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