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检索条件"任意字段=23rd International Conference on Computer Systems and Technologies, CompSysTech 2022"

共 801 条记录，以下是211-220 订阅

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Multi-agent deep reinforcement learning for traffic signal control with Nash Equilibrium 23

Multi-agent deep reinforcement learning for traffic signal c...

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23rd IEEE international conference on High Performance Computing and Communications, 7th IEEE international conference on Data Science and systems, 19th IEEE international conference on Smart City and 7th IEEE international conference on Dependability in Sensor, Cloud and Big Data systems and Applications, HPCC-DSS-SmartCity-DependSys 2021

作者： Wei, Wei Wu, Qiang Wu, Jianqing Du, Bo Shen, Jun Li, Tinghong School of Computer Science and Engineering Xi'an University of Technology Xi'an China Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of China Chengdu China University of Wollongong Smart Infrastructure Facility Wollongong Australia China Mobile Communications Corporation Lanzhou China

ISBN: (纸本)9781665494571

Traffic signal control is an essential and chal-lenging real-world problem, which aims to alleviate traffic congestion by coordinating vehicles' movements at road in-tersections. Deep reinforcement learning (DRL) combines deep neural networks (DNNs) with a framework of reinforcement learning, which is a promising method for adaptive traffic signal control in complex urban traffic networks. Now, multi-agent deep reinforcement learning (MARL) has the potential to deal with traffic signal control at a large scale. However, current traffic signal control systems still rely heavily on simplified rule- based methods in practice. In this paper, we propose: (1) a MARL algorithm based on Nash Equilibrium and DRL, namely Nash Asynchronous Advantage Actor-Critic (Nash-A3C);(2) an urban simulation environment (SENV) to be essentially close to the real-world scenarios. We apply our method in SENV, obtaining better performance than benchmark traffic signal control methods by 22.1%, which proves that Nash-A3C to be more suitable for large industrial level deployment. © 2021 IEEE.

关键词： Performance evaluation Deep learning Computational modeling Roads Neural networks Reinforcement learning Nash equilibrium

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Characterizing OpenMP Synchronization Implementations on ARMv8 Multi-Cores 23

Characterizing OpenMP Synchronization Implementations on ARM...

引用

作者： Wang, Pengyu Gao, Wanrong Fang, Jianbin Huang, Chun Wang, Zheng College of Computer Science National University of Defense Technology China School of Computing University of Leeds United Kingdom

ISBN: (纸本)9781665494571

Synchronization operations like barriers are fre-quently seen in parallel OpenMP programs, where an inefficient implementation can severely limit the application performance. While synchronization optimization has been heavily studied on traditional x86 architectures, there is no consensus on how synchronization can be best implemented on the ARMv8 multi-core CPUs. This paper presents a study of OpenMP synchronization implementation on two representative ARMv8 multi-core architectures, Phytium 2000+ and ThunderX2, by considering various OpenMP synchronization mechanisms offered by two mainstreamed OpenMP compilers, GCC and LLVM. Our evalu-ation compares the performance, overhead and scalability of both compiler implementations. We show that there is no 'one-fits-for-all' synchronization mechanism, and the efficiency of a scheme varies across hardware architectures and thread parallelism. We then share our insights and discuss how OpenMP synchronization operations can be better optimized on emerging ARMv8 multi-cores, offering quantified results for future research directions. © 2021 IEEE.

关键词： Learning systems Context Runtime Smart cities Processor scheduling Scalability Parallel processing

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Sentiment Analysis Method Based on Deep Learning in Adversarial Environment 23

Sentiment Analysis Method Based on Deep Learning in Adversar...

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作者： Lv, Jiamin Yu, Lei Duan, Yucong Inner Mongolia University Department of Computer Science Hohhot China School of Computer and Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9781665494571

With the continuous increase of SaaS (Software as a service) market demand, the competition of SaaS platforms is also increasing. Using deep learning to conduct sentiment analysis on users' reviews can provide a better reference for users to choose SaaS services. But some recent studies have pointed out that deep learning models are easily affected by attackers. When an attacker adds a very small disturbance to the input sample, the deep learning model will give wrong classification results. In order to further study the vulnerability of deep learning models in the field of sentiment analysis, we designed a model of review intention recognition based on DIKW Pyramid, which can recognize the intention of the review, and we also propose an attack algorithm based on synonym substitution of evaluation words and evaluation reasons, which can generate usable and efficient adversarial samples under black box attack. We designed and implemented adversarial attacks on three deep learning models of LSTM, Bi-LSTM and CNN, and combined with Random adversarial attack algorithm, population based adversarial attack algorithm and DeepWordBug adversarial attack algorithm. The experimental results proved that the adversarial attack algorithm based on synonym substitution of evaluation words and evaluation reasons is better than the other three attack algorithms in terms of attack success rate and word replacement rate. © 2021 IEEE.

关键词： Deep learning Sentiment analysis Analytical models Smart cities Software as a service Sociology Classification algorithms

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PipePrune: Pipeline Parallel Based on Convolutional Layer Pruning for Distributed Deep Learning 23

PipePrune: Pipeline Parallel Based on Convolutional Layer Pr...

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作者： Tan, Daojun Jiang, Wenbin Qin, Shang Jin, Hai School of Computer Science and Technology Huazhong University of Science and Technology Natl. Eng. Res. Ctr. for Big Data Technol. and Syst. Serv. Comp. Technol. and Syst. Lab Cluster and Grid Comp. Lab Wuhan430074 China

ISBN: (纸本)9781665494571

Benefitting from the combination of the idea of pipeline with model parallelism and data parallelism, pipeline parallelism improves the efficiency of distributed deep learning systems significantly. However, suffering from the bubbles and gaps caused by the imbalance of different stages in pipeline, it can not output ideal performance yet. To explore the potential of pipeline parallelism further, we propose a novel approach called PipePrune, which adds a convolutional layer pruning strategy to pipeline creatively to reduce the bubbles and gaps. For the convolutional layers with heavy overheads, some unimportant kernels are pruned by the measurement of the L1-norm. This approach makes the processing overheads of different pipeline stages more balanced. The experimental results show that, compared with state-of-the-art pipeline methods, PipePrune can improve the training speeds obviously (e.g. for ResNet50 on ImageNet, about 30% speed improvement is realized with only 1.1% loss of training accuracies). © 2021 IEEE.

关键词： Training Deep learning Smart cities Pipelines Neural networks Distributed databases Parallel processing

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Real-Time Automatic Checkout via Prompt-Based Product Extraction and Cross-Domain Learning 23

Real-Time Automatic Checkout via Prompt-Based Product Extrac...

引用

23rd IEEE international conference on Machine Learning and Applications, ICMLA 2024

作者： Pettersson, Tobias Riveiro, Maria Löfström, Tuwe University of Skövde Jönköping University Itab Shop Products Ab Jönköping Sweden Jönköping University Dept. of Computer Science and Informatics Jönköping Sweden Jönköping University Dept. of Computing Jönköping Sweden

ISBN: (纸本)9798350374889

Automatic checkout systems are designed to predict a complete shopping receipt using an image from the checkout area. These systems require high classification accuracy across numerous classes and must operate in real-time, despite domain differences between training data and real-world conditions. Building on recent advancements, we propose a method that outperforms current solutions and can be applied in real-time in automatic checkout systems. Our method leverages the Segment Anything Model to extract high-quality masks from lab product images, which are then transformed into synthetic checkout images and adapted to the real domain using contrastive unpaired translation. We train a product recognition model with data augmentation, named SCA+Y8, and further improve it through fine-tuning with pseudo-labels from unlabeled checkout images, resulting in an improved model called SCAFT+Y8. SCAFT+Y8 achieves a great increase in state-of-the-art performance, with an average receipt classification accuracy of 97.58%, and shows strong performance in smaller models, indicating the potential for deployment on low-cost edge devices. © 2024 IEEE.

关键词： Object detection

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BOFSanitizer: Efficient locator and detector for buffer overflow vulnerability 23

BOFSanitizer: Efficient locator and detector for buffer over...

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作者： Wang, Wenzhi Fan, Meng Yu, Aimin Meng, Dan Institute of Information Engineering Chinese Academy of Sciences China School of Cyber Security University of Chinese Academy of Sciences Beijing China

ISBN: (纸本)9781665494571

Buffer overflow is one of the most serious and common vulnerability that using memory beyond the allocated memory. It can cause many severe threats, including system crash, arbitrary code execution and data corruption. Buffer overflow vulnerability has two main characteristics, one is that buffer overflow vulnerability may not be discovered after its path has been executed. The other is that not every buffer overflow vulnerability could cause crash. Therefore, existing program analysis tools have more or less deficiencies in detecting buffer overflow vulnerability. This paper presents a BOF-Sanitizer method for buffer overflow vulnerability detection. We proposed a pre-process method that combine vulnerability metric and rank, which can effectively avoid the exploration of unnecessary paths and quickly locate potential bug positions. We present a custom memory model of concolic execution and a boundary detection engine based on a low fat pointer, which not only ensure that the executed path is safe, but also effectively reduces the system performance overhead through the real time interacting with the analysis engine and solver in the process of dynamic detection. We implement a prototype system of BOFSanitizer to detect buffer overflow in different application, the result show that BOFSanitizer accurately discovers 88% buffer overflow target points and the detection rate improve over 50% with compare to simple combination of existing program analysis tools. © 2021 IEEE.

关键词： Measurement Smart cities System performance computer bugs Prototypes Buffer overflows Detectors

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Towards automatisation of academic administrative processes: A Romanian case study 23

Towards automatisation of academic administrative processes:...

引用

23rd international conference on Control systems and computer Science technologies, CSCS 2021

作者： Hogea, Alexandru Ursu, Olimpia Condruz, Cristian Dobre, Ciprian Politehnica University of Bucharest Faculty of Automatic Control and Computers Romania

ISBN: (纸本)9781665439398

This paper describes a pilot case study about how administrative processes can be digitalized. We present details behind a cloud ecosystem that enables researchers and students at Politehnica University of Bucharest to obtain documents needed for traveling in academic purposes a more convenable. Throughout this paper, we will discuss about the case study, our proposed solution, its implementation and further improvements that can be achieved. © 2021 IEEE.

关键词： computer science Ecosystems Process control Control systems

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Perceiving the Narrative Style for Fake News Detection Using Deep Learning 23

Perceiving the Narrative Style for Fake News Detection Using...

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作者： Sun, Mengtao Hameed, Ibrahim A. Wang, Hao Pasquine, Mark Norwegian University of Science and Technology Dept. Ict and Natural Sciences Ålesund Norway Norwegian University of Science and Technology Dept. Computer Science Gjovik Norway Norwegian University of Science and Technology Dept. International Business Ålesund Norway

ISBN: (纸本)9781665494571

Existing deep-learning-based features have shown strong enough results (more than 90% accuracy) if a large amount of annotated data is available. However, in reality, data annotation is labor-intensive and expensive. This work proposes a novel approach for fake news detection by perceiving narrative style with deep learning to alleviate the problem. Deep-learning-based features are represented as embedding vectors. Traditional embedding vectors ingest word context information, but the training requires considerable annotated datasets. Many linguistics studies have shown that written styles, such as the usage of punctuation, repetition of words, and grammatical order, are significant for distinguishing fake news. Our model takes advantage of the word-to-word dependency relationship, describing the styles of the news utterances. We denote the proposed model as Syntax Graphical Thread (SGT) network. We utilize a trainable randomly initialized embedding and Gated Recurrent Unit (GRU) layer to capture the context vector, while a Graph Attention (GAT) layer is used to capture the narrative features. The experimental result manifests that our method can significantly mitigate the reliance on data scale and present better classification results when the dataset is limited. © 2021 IEEE.

关键词： Context Deep learning Training Smart cities Text categorization Syntactics Logic gates

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Forwarding Policy Verification of Hybrid Multi-domain Software-Defined Network 23

Forwarding Policy Verification of Hybrid Multi-domain Softwa...

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作者： Yao, Jiangyuan Jiang, Zheng Yang, Weiping Wang, Minrui Li, Deshun School of Computer Science and Technology Hainan University Haikou China School of Cyberspace Security Hainan University Haikou China

ISBN: (纸本)9781665494571

Software-Defined Network (SDN) is now widely used in the industry and the exploration of multi-domain SDN is currently underway. Because of the different manager, the forwarding policy conflict occurs frequently in multi-domain SDN. However, the previous verification of network policy conflicts is only limited to traditional networks or single domain SDN. Therefore, we carry out our work on hybrid multi-domain SDN which is a type of multi-domain SDN. We merge the SDN flow table entries with border router configuration files to construct a formal model HM-SDN of hybrid multi-domain SDN, and then generate a graph structure SDNIG according to HM-SDN which can depict the real network environment, finally present a verifier VeriGraph based on graph algorithms. After evaluation, we can construct our graph structure no more than 0.13s and can verify common attributes less than 3s in the small and medium networks. © 2021 IEEE.

关键词： Industries Smart cities Hybrid power systems Servers

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Multiscale Spatial-spectral Joint Feature Learning for Multispectral and Hyperspectral Image Fusion 23

Multiscale Spatial-spectral Joint Feature Learning for Multi...

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作者： Min, Zhichao Wang, Yifan Jia, Sen College of Computer Science and Software Engineering Shenzhen University China

ISBN: (纸本)9781665494571

For RGB image super-resolution, usually operates on a single image. However, due to a large number of spectral bands and high dimensionality of the data in hyperspectral images (HSIs), it is difficult for a single image super-resolution to improve spatial resolution without spectral distortion. Therefore, it is necessary to fuse spatial features of multispectral images (MSIs) to improve spatial resolution. In this paper, we take advantage of the end-to-end advantages of deep learning and propose multiscale spatial-spectral joint feature learning (MSSJFL) for multispectral and hyperspectral image fusion. MSSJFL method utilizes different scale feature learning to exact single spatial feature, spectral feature, spatial-spectral joint feature, and then restore spectral information and spatial details via feature restoration module. Experimental results demonstrate that the proposed network architecture achieves better performance compared with state-of-the-art HSI and MSI fusion. The classification accuracy of the fusion result on the real HSI dataset is also closest to the ground truth. The code of MSSJFL is available at https://***/jx-mzc/MSSJFL. © 2021 IEEE.

关键词： Representation learning Deep learning Smart cities Superresolution Network architecture Feature extraction Image restoration

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