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Quantitative analysis of real-time performance and hardware requirements for edge computing platform

Data Science and Informetrics 2021年第2期1卷 47-63页

作者： Kangkang Shi Gangyong Jia Youhuizi Li Yuyu Yin Congfeng Jiang Li Zhou School of Computer Science Hangzhou Dianzi UniversityHangzhouChina Key Laboratory of Complex Systems Modeling and Simulation Hangzhou Dianzi UniversityHangzhouChina

For real-time edge systems such as autonomous driving,not only the correctness of task functions,but also the response and processing time of tasks should be *** the hardware selection phase of a real-time system,time series analyses must be performed on the hardware platform running real-time *** present,the common method of worst-case execution time(WCET)analysis focuses mainly on analyzing the impact of hardware platform architecture or task execution process on the task running ***,different tasks in an autopilot system have different levels of urgency,and preemption between tasks is the main factor that affects the task execution *** key problem is how to quantify the time fluctuation caused by task preemption for each subtask of the autopilot system running on a fixed hardware *** paper presents a time analysis method for a real-time application based on a queuing theory and preemptive scheduling strategy,which assigns different priorities to tasks according to their time urgency and preemptive scheduling according to task *** an experimental case study,the impact of the running time of each subtask in a real-time application with task priority preemptive scheduling is analyzed,along with the impact of changes in hardware platform performance on such real-time applications.

关键词： Autonomous Driving Queuing Theory Preemptive Scheduling Time Series Analysis

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A Comprehensive Survey of 3D Dense Captioning: Localizing and Describing Objects in 3D Scenes

arXiv

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arXiv 2024年

作者： Yu, Ting Lin, Xiaojun Wang, Shuhui Sheng, Weiguo Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Three-Dimensional (3D) dense captioning is an emerging vision-language bridging task that aims to generate multiple detailed and accurate descriptions for 3D scenes. It presents significant potential and challenges due to its closer representation of the real world compared to 2D visual captioning, as well as complexities in data collection and processing of 3D point cloud sources. Despite the popularity and success of existing methods, there is a lack of comprehensive surveys summarizing the advancements in this field, which hinders its progress. In this paper, we provide a comprehensive review of 3D dense captioning, covering task definition, architecture classification, dataset analysis, evaluation metrics, and in-depth prosperity discussions. Based on a synthesis of previous literature, we refine a standard pipeline that serves as a common paradigm for existing methods. We also introduce a clear taxonomy of existing models, summarize technologies involved in different modules, and conduct detailed experiment analysis. Instead of a chronological order introduction, we categorize the methods into different classes to facilitate exploration and analysis of the differences and connections among existing techniques. We also provide a reading guideline to assist readers with different backgrounds and purposes in reading efficiently. Furthermore, we propose a series of promising future directions for 3D dense captioning by identifying challenges and aligning them with the development of related tasks, offering valuable insights and inspiring future research in this field. Our aim is to provide a comprehensive understanding of 3D dense captioning, foster further investigations, and contribute to the development of novel applications in multimedia and related domains. Copyright © 2024, The Authors. All rights reserved.

关键词： Data handling

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Privacy sensitive large-margin model for face de-identification 1st

Privacy sensitive large-margin model for face de-identifica...

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1st International Conference on Neural Computing for Advanced Applications, NCAA 2020

作者： Guo, Zhiqiang Liu, Huigui Kuang, Zhenzhong Nakashima, Yuta Babaguchi, Noboru Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Osaka University Osaka Japan

ISBN: (纸本)9789811576690

There is an increasing concern of face privacy protection along with the wide application of big media data and social networks due to free online data release. Although some pioneering works obtained some achievements, they are not sufficient enough for sanitizing the sensitive identity information. In this paper, we propose a generative approach to de-identify face images yet preserving the non-sensitive information for data reusability. To ensure a high privacy level, we introduce a large-margin model for the synthesized new identities by keeping a safe distance with both the input identity and existing identities. Besides, we show that our face de-identification operation follows the (Formula Presented)-differential privacy rule which can provide a rigorous privacy notion in theory. We evaluate the proposed approach using the vggface dataset and compare with several state-of-the-art methods. The results show that our approach outperforms previous solutions for effective face privacy protection while preserving the major utilities. © Springer Nature Singapore Pte Ltd. 2020.

关键词： Privacy-preserving techniques

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Prompting Video-Language Foundation Models with Domain-specific Fine-grained Heuristics for Video Question Answering

arXiv

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arXiv 2024年

作者： Yu, Ting Fu, Kunhao Wang, Shuhui Huang, Qingming Yu, Jun School of Information Science and Technology Hangzhou Normal University Hangzhou311121 China Key Laboratory of Intelligent Information Processing Institute of Computing Technology Chinese Academy of Sciences Beijing100190 China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China

Video Question Answering (VideoQA) represents a crucial intersection between video understanding and language processing, requiring both discriminative unimodal comprehension and sophisticated cross-modal interaction for accurate inference. Despite advancements in multi-modal pre-trained models and video-language foundation models, these systems often struggle with domain-specific VideoQA due to their generalized pre-training objectives. Addressing this gap necessitates bridging the divide between broad cross-modal knowledge and the specific inference demands of VideoQA tasks. To this end, we introduce HeurVidQA, a framework that leverages domain-specific entity-action heuristics to refine pre-trained video-language foundation models. Our approach treats these models as implicit knowledge engines, employing domain-specific entity-action prompters to direct the model’s focus toward precise cues that enhance reasoning. By delivering fine-grained heuristics, we improve the model’s ability to identify and interpret key entities and actions, thereby enhancing its reasoning capabilities. Extensive evaluations across multiple VideoQA datasets demonstrate that our method significantly outperforms existing models, underscoring the importance of integrating domain-specific knowledge into video-language models for more accurate and context-aware VideoQA. Copyright © 2024, The Authors. All rights reserved.

关键词： Inference engines

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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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IEEE International Conference on Communications in China (ICCC)

作者： Mengchu Xu Yan Zeng Meiting Xue Ji-Lin Zhang Jian Wan Mingyao Zhou Yilin Wen Yukun Shi School of Computer Science and Technology Hangzhou Dianzi University 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 School of Cyberspace Hangzhou Dianzi University Hangzhou China Huawei Enterprise Communication Technology Co Hangzhou China

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.

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Short-Term Traffic Flow Prediction Method Based on Spatio-Temporal Characteristics of complex Road Network

Short-Term Traffic Flow Prediction Method Based on Spatio-Te...

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作者： Huawei ZHU Yihao MAO Yiming ZHAO Liyuan SHI Yanli SHAO School of Information and Electrical Engineering Zhejiang University City College Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and TechnologyHangzhou Dianzi University

With the development of urbanization,the number of residents' motor vehicles has increased sharply,and traffic congestion problem has become increasingly *** construction of Intelligent Traffic System(ITS) has become the main means to alleviate traffic ***-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 ***,a graph convolutional network(GCN) capable of processing non-Euclidean data structures is used to extract the spatial characteristics of traffic flow ***,the long and short-term memory(LSTM) neural network is used to process the time ***,the two are combined to realize the effective processing of the spatio-temporal characteristics of traffic flow *** 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.

关键词： Traffic Flow Prediction Spatio-temporal GCN LSTM

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An Auto-Parallel Method for Deep Learning Models Based on Genetic Algorithm

An Auto-Parallel Method for Deep Learning Models Based on Ge...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Yan Zeng ChengChuang Huang YiJie Ni ChunBao Zhou JiLin Zhang Jue Wang MingYao Zhou MeiTing Xue YunQuan Zhang School of Computer Science and Technology Hangzhou Dianzi University Hangzhou China Key Laboratory for Modeling and Simulation of Complex Systems Ministry of Education Hangzhou China Data Security Governance Zhejiang Engineering Research Center Hangzhou China School of ITMO Joint Institute Hangzhou Dianzi University Hangzhou China Institute of Computer Network Information Center of the Chinese Academy of Sciences Beijing China HuaWei State Key Laboratory of Computer Architecture Institute of Computing Technology of the Chinese Academy of Sciences Beijing China

As the size of datasets and neural network models increases, automatic parallelization methods for models have become a research hotspot in recent years. The existing auto-parallel methods based on machine learning or graph algorithms still have issues with search efficiency and applicability. This paper proposes an automatic parallel method based on a dual-population genetic algorithm, TGA, which transforms model partitioning and placement into an integer linear programming problem and constructs a cost model to evaluate the solution. The solution space is built using the neural network’s dataflow graph and device cluster’s topology, and the dual-population genetic algorithm is used to search for the optimal model parallel strategy. Experiments with various models show that the proposed method can improve single-step execution time by up to 42% compared to the Baechi method and up to 37.7% compared to the Hierarchical method.

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DeepDir: a deep learning approach for API directive detection

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Science China(Information Sciences) 2021年第9期64卷 238-240页

作者： Jingxuan ZHANG He JIANG Shuai LU Ge LI Xin CHEN College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics State Key Laboratory for Novel Software Technology Nanjing University Key Laboratory of Complex Systems Modeling and Simulation Ministry of Education School of Software Dalian University of Technology School of Electronic Engineering and Computer Science Peking University School of Computer Science and Technology Hangzhou Dianzi University

Dear editor,Software developers tend to reuse existing libraries to facilitate their development process and implement certain functionalities by invoking application programming interfaces(APIs) [1]. However, it remains a challenging task for developers to correctly use APIs [2], so they often consult API learning resources [3, 4]. As one of the most important API learning resources,

关键词： Information systems and Communication Service

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A Fast Algorithm for Solving a Class of the Linear Complementarity Problem in a Finite Number of Steps

Abstract and Applied Analysis

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Abstract and Applied Analysis 2020年第none期2020卷 1-8页

作者： Achik, Yamna Idmbarek, Asmaa Nafia, Hajar Agmour, Imane El Foutayeni, Youssef Analysis Modeling and Simulation Laboratory Hassan II University Morocco Unit for Mathematical and Computer Modeling of Complex Systems IRD France

The linear complementarity problem is receiving a lot of attention and has been studied extensively. Recently, El foutayeni et al. have contributed many works that aim to solve this mysterious problem. However, many results exist and give good approximations of the linear complementarity problem solutions. The major drawback of many existing methods resides in the fact that, for large systems, they require a large number of operations during each iteration;also, they consume large amounts of memory and computation time. This is the reason which drives us to create an algorithm with a finite number of steps to solve this kind of problem with a reduced number of iterations compared to existing methods. In addition, we consider a new class of matrices called the E-matrix. © 2020 Yamna Achik et al.

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Multi-task Paired Masking with Alignment modeling for Medical Vision-Language Pre-training

arXiv

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arXiv 2023年

作者： Zhang, Ke Yang, Yan Yu, Jun Jiang, Hanliang Fan, Jianping Huang, Qingming Han, Weidong The Key Laboratory of Complex Systems Modeling and Simulation School of Computer Science and Technology Hangzhou Dianzi University Hangzhou310018 China Regional Medical Center for National Institute of Respiratory Diseases Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou310016 China AI Lab at Lenovo Research 100094 China The School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China The Department of Medical Oncology at Sir Run Run Shaw Hospital College of Medicine Zhejiang University Hangzhou China The College of Mathematical Medicine Zhejiang Normal University Jinhua China

In recent years, the growing demand for medical imaging diagnosis has placed a significant burden on radiologists. As a solution, Medical Vision-Language Pre-training (Med-VLP) methods have been proposed to learn universal representations from medical images and reports, benefiting downstream tasks without requiring fine-grained annotations. However, existing methods have overlooked the importance of cross-modal alignment in joint image-text reconstruction, resulting in insufficient cross-modal interaction. To address this limitation, we propose a unified Med-VLP framework based on Multi-task Paired Masking with Alignment (MPMA) to integrate the cross-modal alignment task into the joint image-text reconstruction framework to achieve more comprehensive cross-modal interaction, while a Global and Local Alignment (GLA) module is designed to assist self-supervised paradigm in obtaining semantic representations with rich domain knowledge. Furthermore, we introduce a Memory-Augmented Cross-Modal Fusion (MA-CMF) module to fully integrate visual information to assist report reconstruction and fuse the multi-modal representations adequately. Experimental results demonstrate that the proposed unified approach outperforms previous methods in all downstream tasks, including uni-modal, cross-modal, and multimodal tasks. Copyright © 2023, The Authors. All rights reserved.

关键词： Alignment

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