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2021 International Conference on Applied Mathematics, Modeling and Computer simulation, AMMCS 2021

作者： Zhu, Huawei Mao, Yihao Zhao, Yiming Shi, Liyuan Shao, Yanli School of Information and Electrical Engineering Zhejiang University City College Hangzhou310015 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

ISBN: (纸本)9781643682549

With the development of urbanization, the number of residents' motor vehicles has increased sharply, and traffic congestion problem has become increasingly serious. The construction of Intelligent Traffic System (ITS) has become the main means to alleviate traffic congestion. Short-term traffic flow prediction has guiding significance for residents' travel planning and intelligent management of transportation, and has become one of the research hotspots in intelligent transportation ***, A short-term traffic flow prediction method based on the spatio-temporal characteristics of complex road networks is proposed to further improve the prediction accuracy and reduce the prediction cost. Firstly, a graph convolutional network (GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow data. Then, the long and short-term memory (LSTM) neural network is used to process the time characteristics. Finally, the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow data. Experimental results on the real traffic flow dataset prove the feasibility and effectiveness of the proposed method, and can provide a basis for intelligent traffic control and smart city construction. © 2022 The authors and IOS Press.

关键词： Traffic congestion

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An Anomaly Detection Method Based on Adaptive Log and Dual Feature Fusion Analysis for Distributed systems

An Anomaly Detection Method Based on Adaptive Log and Dual F...

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Information systems and Computing technology (ISCTech), International Conference on

作者： Yijiang Jia Kefei Li Ping Lu Baodi Xie Huayang Wang Jincai Chen School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China Wuhan National Laboratory for Optoelectronics Huazhong University of Science and Technology Wuhan China National Key Laboratory of Modeling and Simulation for Complex Systems Beijing China

In this study, we present a middleware-based approach for detecting anomalies in distributed systems. Our method facilitates the dynamic collection of logs at various levels of detail and incorporates an a priori dictionary-based compression strategy to process and transmit logs, minimizing the performance impact on distributed systems. Additionally, we utilize a dual feature fusion technique to analyze the logs. We evaluate the effectiveness of our approach by performing anomaly detection in a publish/subscribe distributed system and comparing it with existing methods. The results illustrate that our method outperforms other approaches, demonstrating superior performance.

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Prompting Large Language Models with Answer Heuristics for Knowledge-Based Visual Question Answering

Prompting Large Language Models with Answer Heuristics for K...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Zhenwei Shao Zhou Yu Meng Wang Jun Yu Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University China School of Computer Science and Information Engineering Hefei University of Technology China

Knowledge-based visual question answering (VQA) requires external knowledge beyond the image to answer the question. Early studies retrieve required knowledge from explicit knowledge bases (KBs), which often introduces irrelevant information to the question, hence restricting the performance of their models. Recent works have sought to use a large language model (i.e., GPT-3 [3]) as an implicit knowledge engine to acquire the necessary knowledge for answering. Despite the encouraging results achieved by these methods, we argue that they have not fully activated the capacity of GPT-3 as the provided input information is insufficient. In this paper, we present Prophet-a conceptually simple framework designed to $prompt$ GPT-3 with answer heuristics for knowledge-based VQA. Specifically, we first train a vanilla VQA model on a specific knowledge-based VQA dataset without external knowledge. After that, we extract two types of complementary answer heuristics from the model: answer candidates and answer-aware examples. Finally, the two types of answer heuristics are encoded into the prompts to enable GPT-3 to better comprehend the task thus enhancing its capacity. Prophet significantly outperforms all existing state-of-the-art methods on two challenging knowledge-based VQA datasets, OK-VQA and A-OKVQA, delivering 61.1% and 55.7% accuracies on their testing sets, respectively.

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FedAG:A Federated Learning Method Based on Data Importance Weighted Aggregation

FedAG:A Federated Learning Method Based on Data Importance W...

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2023 IEEE/CIC International Conference on Communications in China, ICCC 2023

作者： Xu, Mengchu Zeng, Yan Xue, Meiting Zhang, Ji-Lin Wan, Jian Zhou, Mingyao Wen, Yilin Shi, Yukun Hangzhou Dianzi University School of Computer Science and Technology Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education Hangzhou China Zhejiang Engineering Research Center of Data Security Governance Hangzhou China Hangzhou Dianzi University School of Cyberspace Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

ISBN: (纸本)9798350345384

Federated learning is a distributed machine learning approach that trains models with multiple clients and data locally. However, the existing methods ignore the differences between local models caused by the data heterogeneity, and give a high aggregation weight for the low-quality local model, which leads to low accuracy of the global model. For this problem, we propose a federated learning method based on data importance weighted aggregation, FedAG. It constructs an evaluation model of data importance based on the data distribution skewness, data volume, and data quality of clients, and aggregates local models with different weights computed by the evaluation model of data importance into a global model. We make experiments on five datasets including EMNIST, MNIST, and CIFAR-10. The results show that the accuracy of FedAG is respectively improved by 6.5%, 1.8%, 1.3% and 2.1%, compared with FedAvg, FedProx, FedVar, and Scaffold algorithms. © 2023 IEEE.

关键词： Scaffolds

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Discovering API Directives from API Specifications with Text Classification

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Journal of Computer science & technology 2021年第4期36卷 922-943页

作者： Jing-Xuan Zhang Chuan-Qi Tao Zhi-Qiu Huang Xin Chen College of Compute Science and Technology Nanjing University of Aeronautics and AstronauticsNanjing 210016China Key Laboratory of Safety-Critical Software(Nanjing University of Aeronautics and Astronautics) Ministry of Industry and Information TechnologyNanjing 210016China Key Laboratory of Complex Systems Modeling and Simulation(Hangzhou Dianzi University) Ministry of Education Hangzhou 310018China School of Computer Science and Technology Hangzhou Dianzi UniversityHangzhou 310018China

Application programming interface(API)libraries are extensively used by *** correctly program with APIs and avoid bugs,developers shall pay attention to API directives,which illustrate the constraints of ***,API directives usually have diverse morphologies,making it time-consuming and error-prone for developers to discover all the relevant API *** this paper,we propose an approach leveraging text classification to discover API directives from API ***,given a set of training sentences in API specifications,our approach first characterizes each sentence by three groups of ***,to deal with the unequal distribution between API directives and non-directives,our approach employs an under-sampling strategy to split the imbalanced training set into several subsets and trains several *** a new sentence in an API specification,our approach synthesizes the trained classifiers to predict whether it is an API *** have evaluated our approach over a publicly available annotated API directive *** experimental results reveal that our approach achieves an F-measure value of up to 82.08%.In addition,our approach statistically outperforms the state-of-the-art approach by up to 29.67%in terms of F-measure.

关键词： Application programming interface(API)directive API specification imbalanced learning text classification

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Analysis of Zero-Day Virus Suppression Strategy based on Moving Target Defense

Analysis of Zero-Day Virus Suppression Strategy based on Mov...

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2021 IEEE International Conference on Signal Processing, Communications and Computing, ICSPCC 2021

作者： Mingyang, Qiu Qingwei, Meng Yan, Fu Xikang, Wang Information and Navigation College Air Force Engineering University Xi'an China Science and Technology on Complex Aviation Systems Simulation Laboratory Beijing China

ISBN: (纸本)9781665429184

In order to suppress the spread of zero-day virus in the network effectively, a zero-day virus suppression strategy was proposed. Based on the mechanism of zero-day virus transmission and the idea of platform dynamic defense, the corresponding methods of virus transmission suppression are put forward. By changing the platform switching frequency, the scale of zero-day virus transmission and its inhibition effect are simulated in a small-world network model. Theory and computer simulation results show that the idea of platform switching can effectively restrain the spread of virus. © 2021 IEEE.

关键词： simulation platform

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A coevolutionary framework of business-IT alignment via the lens of enterprise architecture

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Journal of systems Engineering and Electronics 2020年第5期31卷 983-995页

作者： LIN Menglong YI Shuanghui ZHANG Mengmeng CHEN Tao CHEN Honghui ZHANG Xiaoxue Science and Technology on Information Systems Engineering Laboratory National University of Defense TechnologyChangsha 410072China National Key Laboratory for Complex System Simulation Academy of Military SciencesBeijing 100000China

Due to the turbulent external business environment, the complexity of internal relations of the organization and the emergence of subversive IT roles, the business-IT alignment(BITA)has become increasingly difficult. The unsuccessful realization of BITA will lead to the waste of organizational resources, the reduction of return on investment and eventually the loss of competitive advantage. In recent years, coevolution has received widespread attention due to its ability to describe the dynamic relationship between IT and business. Multiple principles such as quickening learning action loops and adopt suitable organizing principles for achieving business and IT coevolution(BITC) are obtained. However, the continuous BITC is still hard to be achieved because of the lack of complete BITC management. This paper focuses on the management process of the BITC and how to perform it gradually. A coevolution framework combines the enterprise architecture(EA) approach with the coevolution analysis is proposed, which contains the design of EA, the sensing and governance of the misalignment and the procedure of the EA misalignment *** steps for the governance and prevention of misalignment are discussed in particular. Through comparison with the principles,characteristics and methods of coevolution in the literature, the proposed framework is evaluated. The results show that the proposed framework is effective for BITC implementation.

关键词： business-IT alignment(BITA) coevolution misalignment governance misalignment prevention enterprise architecture

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Strength of the North African monsoon in the Last Interglacial and under future warming

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Atmospheric and Oceanic science Letters 2023年第3期16卷 20-26页

作者： Jiazhi He Weiyi Sun Jing Wang Bin Wang Jian Liu Key Laboratory for Virtual Geographic Environment Ministry of EducationState Key Laboratory Cultivation Base of Geographical Environment Evolution of Jiangsu ProvinceJiangsu Center for Collaborative Innovation in Geographical Information Resource Development and ApplicationSchool of Geography ScienceNanjing Normal UniversityNanjingChina Department of Atmospheric Sciences and AtmosphereOcean Research Center University of Hawaii at ManoaHonoluluHIUA Jiangsu Provincial Key Laboratory for Numerical Simulation of Large Scale Complex Systems School of Mathematical ScienceNanjing Normal UniversityNanjingChina Open Studioforthe Simulationof Ocean-Climate-Isotope Qingdao National Laboratoryfor Marine Science and TechnologyQingdaoChina

与人为强迫引起的全球变暖相比,末次间冰期是轨道强迫引起的过去80万年来最暖的一个间冰期,但鲜有人研究末次间冰期中北非季风的响应.因此,本文基于CMIP6多模式模拟结果对比研究了末次间冰期和SSP5-8.5情景下北非季风的响应,发现末次间... 详细信息

与人为强迫引起的全球变暖相比,末次间冰期是轨道强迫引起的过去80万年来最暖的一个间冰期,但鲜有人研究末次间冰期中北非季风的响应.因此,本文基于CMIP6多模式模拟结果对比研究了末次间冰期和SSP5-8.5情景下北非季风的响应,发现末次间冰期下北非季风平均降水及其降水变率均远大于SSP5-8.5情景下的结果.轨道强迫导致的北大西洋暖于南大西洋增加了北非季风环流和平均降水,同时,南北大西洋海温梯度变化通过增强热带北大西洋的海气相互作用增大了海温梯度和撒哈拉低压的变率,从而增强了北非季风降水变率.

关键词：北非季风降水变率末次间冰期全球变暖

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Evolutionary Interest Representation Network for Click-Through Rate Prediction

Evolutionary Interest Representation Network for Click-Throu...

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IEEE International Conference on Web Services (ICWS)

作者： Renjie Zhou Zhenming Jin Jian Wan Jilin Zhang Xingyu Guo Qiang Liu Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory of Complex Systems Modeling and Simulation of the Ministry of Education ITMO Joint Institute Hangzhou Dianzi University Hangzhou China School of Information and Electronic Engineering Zhejiang University of Science and Technology Hangzhou China Dept. of Computer and Information Technology Zhejiang Police College Hangzhou China

The rapid development of the Internet has revo-lutionized our lives, providing us with an array of convenient services. However, this revolution has also led to the problem of data overload. Personalized recommendation services have been widely recognized as an effective solution to this problem by both the industrial and academic communities. Accurate prediction of click-through rate (CTR) is a critical task in personalized recommendation systems since it can improve the user’s shopping experience, ultimately increasing revenue for the platform. To achieve accurate CTR prediction, it is crucial to capture user interests. While several methods for interest modeling exist, challenges related to user interest evolution and user behavior noise need further attention. This paper proposes a novel CTR prediction model named the Evolutionary Interest Representation Network (EIRN), which learns the evolving interest representation of users from their behaviors, profiles, and environmental attributes. In the model, we introduce an advertisement correlation graph decomposition layer and a noise filter layer to enhance and purify the user behavior representation. An interest extraction layer is designed to capture sequential correlations and common characteristics between user behaviors, representing their interests. Additionally, as user interests evolve over time and their importance changes, an attention-based interest evolution layer is designed to track the evolution of user interests. Our proposed method is validated using four public datasets and an industrial dataset, showing its superiority over existing CTR prediction methods.

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Exploring the Physical World Adversarial Robustness of Vehicle Detection

arXiv

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arXiv 2023年

作者： Jiang, Wei Zhang, Tianyuan Liu, Shuangcheng Ji, Weiyu Zhang, Zichao Xiao, Gang Information Science Academy China Eletronics Technology Group Corporation Beijing100846 China Beihang University Beijing China National Key Laboratory for Complex Systems Simulation Beijing China

Adversarial attacks can compromise the robustness of real-world detection models. However, evaluating these models under real-world conditions poses challenges due to resource-intensive experiments. Virtual simulations offer an alternative, but the absence of standardized benchmarks hampers progress. Addressing this, we propose an innovative instant-level data generation pipeline using the CARLA simulator. Through this pipeline, we establish the Discrete and Continuous Instant-level (DCI) dataset, enabling comprehensive experiments involving three detection models and three physical adversarial attacks. Our findings highlight diverse model performances under adversarial conditions. Yolo v6 demonstrates remarkable resilience, experiencing just a marginal 6.59% average drop in average precision (AP). In contrast, the ASA attack yields a substantial 14.51% average AP reduction, twice the effect of other algorithms. We also note that static scenes yield higher recognition AP values, and outcomes remain relatively consistent across varying weather conditions. Intriguingly, our study suggests that advancements in adversarial attack algorithms may be approaching its "limitation".In summary, our work underscores the significance of adversarial attacks in real-world contexts and introduces the DCI dataset as a versatile benchmark. Our findings provide valuable insights for enhancing the robustness of detection models and offer guidance for future research endeavors in the realm of adversarial attacks. Copyright © 2023, The Authors. All rights reserved.

关键词： Pipelines

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