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STHGN: Citywide Crowd Flow Prediction in Irregular Regions using Hypergraph Convolutional network 29

STHGN: Citywide Crowd Flow Prediction in Irregular Regions u...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Xing, Jintao Xing, Weiwei Wei, Xiang Zhang, Jian Lu, Wei Beijing Jiaotong University School of Software Engineering Beijing100044 China

ISBN: (纸本)9798350330717

Forecasting crowd movement accurately across an urban area is crucial for efficient traffic control and ensuring public security. Current methods involve transforming the city's roadmap into a grid-based map, enabling Convolutional Neural networks (CNNs) or Graph Convolutional networks (GCNs) to capture spatio-temporal relationships efficiently. However, this approach overlooks the connection between irregularly shaped real-world areas, which can be categorized into various functional zones. In this article, we introduce a novel approach for predicting urban crowd flow named STHGN, which utilizes hypergraph convolutional networks. By constructing 3-level hypergraphs from irregular areas and adopting Hyper-GCN, we capture mobility among irregular regions. We construct the hypergraphs based on hour, day, and week, simultaneously using gated-based mechanisms to fuse various embeddings. We evaluate the efficacy of our model by contrasting it with 11 other approaches, including the most sophisticated STGs. After conducting numerous experiments, we find that STHGN outperforms these methods with higher accuracy, resulting in a reduction of approximately 6-9% in mean absolute error (MAE) for crowd flow prediction. © 2023 IEEE.

关键词： Crowd Flow Prediction Hypergraph Convolutional network Irregular regions Time series forecasting

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Control of Unmanned Bicycle based on Tensor Product Model Transformation and Hammersley Sampling Method 12

Control of Unmanned Bicycle Based on Tensor Product Model Tr...

引用

12th International conference on Intelligent Control and Information processing, ICICIP 2024

作者： Xie, Depeng Wang, Degang Zhao, Guoliang Li, Hongxing School of Control Science and Engineering Dalian University of Technology Dalian China College of Electronic Information Engineering Inner Mongolia University Hohhot China

ISBN: (纸本)9798350308020

In this paper, path planning and balance control of unmanned bicycles are investigated. Firstly, the Deep Deterministic Policy Gradient (DDPG) algorithm is used to train the Artificial Potential Field (APF) algorithm to implement path planning in complex maps. Secondly, a dual closed-loop control strategy is adopted for the path tracking and the balance control. In the inner-loop control, the Hammersley sampling (HS) method is used to obtain the hyper-cube network model of the unmanned bicycle, and the balance controller is designed based on the tensor product (TP) model transformation and parallel distributed compensation (PDC) method for stabilization of the unmanned bicycle. The outer-loop control system uses the Ackermann steering geometry model to track the planned path. Finally, the simulation results demonstrate that the path planning algorithm plans the path that satisfies the constraints and the designed balance controller can keep the unmanned bicycle balanced well during trajectory tracking. © 2024 IEEE.

关键词： Motion planning

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Swarm: A High-Performance Asynchronous BFT Protocol Adapted to High network Delay 29

Swarm: A High-Performance Asynchronous BFT Protocol Adapted ...

引用

29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Han, Jianyu Liu, Xiulong Li, Keqiu Tianjin University College of Intelligence and Computing China China

ISBN: (纸本)9798350330717

Recently, asynchronous byzantine fault tolerance (BFT) consensus has made progress in linearized communication complexity, allowing nodes to broadcast a small number of transactions at their own pace in networks with variable bandwidth. However, this means that network propagation delay poses a more severe bottleneck to the system compared to transmission delay affected by block size and bandwidth. Particularly in networks with high propagation delays, nodes need to wait more for messages to propagate through the network, which severely hinders system performance. We propose Swarm, an asynchronous BFT protocol that is suitable for networks with tight bandwidth and propagation delay. The new protocol structure supports highly concurrent propagation-delay-intensive and propagationdelay-tolerant stages by separating the control and data layers and ensures that nodes achieve an ordered and consistent log for parallel blocks. We also design a group broadcasting strategy that allows nodes to not broadcast proposals from other nodes without compromising safety and liveness while reducing communication cost. Importantly, our work is orthogonal to existing improvements based on communication complexity and bandwidth and does not sacrifice existing results. We built a complete system prototype and conducted a thorough evaluation. The results show that Swarm performs better in networks with higher propagation delays, achieving 2-5x TPS and reducing latency by 50% compared to existing asynchronous protocols for different scales. © 2023 IEEE.

关键词： Asynchronous consensus Blockchain Byzantine fault tolerance

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Federated Anomaly Detection with Isolation Forest for IoT network Traffics 29

Federated Anomaly Detection with Isolation Forest for IoT Ne...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Li, Junyan Zhang, Xuyun Xiang, Haolong Beheshti, Amin Macquarie University Australia

ISBN: (纸本)9798350330717

With the development of modern technology, the application of various types of devices in life has become more extensive, especially with the emergence of the Internet of Things (IoT), which makes a large number of devices to be connected to the network. However, while bringing convenience to life, attacks against IoT devices have also begun to appear and become one of the most concerned issues. These IoT devices produce and transmit data containing important user information every second. Attackers are targeting this characteristic to initiate further malicious attacks on these valuable data. In order to protect users from such attacks, it is important that threats can be detected and identified in time before they cause damage. To address this issue, this paper first reviewed the current state-of-the-art anomaly detection methods and based on the finding of uncovered areas of existing methods, this paper proposed a new anomaly detection framework leveraging the combination of Federated Learning and Isolation Forest. The framework performs tree construction on the clients' end and further uploads the encrypted data containing the nodes' information of the trees to the central server for the forest construction, after multiple interactions, the abnormal behaviour in the clients could be able to be identified more effectively and ultimately improved the accuracy of the detection results while protecting the privacy of the client data. © 2023 IEEE.

关键词： Anomaly Detection Cybersecurity Federated Learning IoT network

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An Iterative Cleaning Method for Redundant Data in Multi-Source Heterogeneous Data based on Decision Tree Algorithm 4

An Iterative Cleaning Method for Redundant Data in Multi-Sou...

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4th International conference on Information Science, parallel and distributed Systems, ISPDS 2023

作者： Hou, Jiaxuan Huang, Wenqi Liang, Lingyu Cao, Shang Zhang, Huanming Zhao, Xiangyu Li, Hanju Digital Grid Research Institute China Southern Power Grid. Guangdong Guangzhou510663 China

ISBN: (纸本)9798350337181

The current conventional iterative cleaning method for multi-source heterogeneous data mainly calculates the data repetition to achieve the redundant data rejection, which leads to poor data cleaning effect due to the lack of parsimonious processing for data. In this regard, an iterative cleaning method based on decision tree algorithm is proposed for redundant data in multi-source heterogeneous data. By calculating the information gain value of redundant data in multi-source heterogeneous data, and using the highest value as the classification criterion, a decision tree is constructed to realize data classification. And the deep belief network model is constructed to extract the data mixture features, and finally the data is iteratively cleaned by data table repetition judgment rule pairs for data parsimony. In the experiments, the proposed method is verified for the cleaning effect. The experimental results show that when the proposed method is used to clean multi-source heterogeneous data, the data checking accuracy is high and has a more desirable data cleaning performance. © 2023 IEEE.

关键词： Decision trees

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Optimizing Secure Information Interaction in distributed Computing Systems by the Sequential Concessions Method 28

Optimizing Secure Information Interaction in Distributed Com...

引用

28th euromicro International conference on parallel, distributed and network-based processing (PDP)

作者： Kotenko, Igor Sineshchuk, Yury Saenko, Igor Russian Acad Sci SPIIRAS St Petersburg Inst Informat & Automat St Petersburg Russia St Petersburg Univ Minist Internal Affairs Russian Federat St Petersburg Russia

ISBN: (纸本)9781728165820

The paper proposes a new approach to multicriteria optimization of the structure of secure information interaction in distributed computing systems. A distinguishing feature of the approach is the use of the sequential concessions method. This approach provides the acceptable flexibility in comparing the system construction variants by taking into account not only the priority of the system according to efficiency indicators, but also the relationships of the indicators. The mathematical basis of the proposed method is considered. The procedure of implementation of the proposed method for ensuring security of information interaction in a distributed hierarchical automated control system is demonstrated, and its experimental assessment is given.

关键词： distributed computer network cyber-physical systems optimization method of sequential concessions

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High-Order Faithful Interpretation Methods for Tensor-based CNNs 21

High-Order Faithful Interpretation Methods for Tensor-Based ...

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21st IEEE International Symposium on parallel and distributed processing with Applications, 13th IEEE International conference on Big Data and Cloud Computing, 16th IEEE International conference on Social Computing and networking and 13th International conference on Sustainable Computing and Communications, ISPA/BDCloud/SocialCom/SustainCom 2023

作者： Chen, Pengxu Liu, Huazhong Zhang, Hanning Ding, Jihong Zhang, Yunfan Chen, Dehua Hainan University School of Comp. Sci. & Tech Haikou China Haikou China Donghua University School of Comp. Sci. & Tech Shanghai China

ISBN: (纸本)9798350329223

Tensor-based Convolution Neural networks (CNNs) have achieved remarkable success in realizing multi-modal classification while mining the hidden relationships of big data. Nevertheless, these models exhibit high performance but low interpretability due to the numerous intricate and abstract internal network structures. Therefore, this paper focuses on exploring interpretation methods suitable for tensor-based CNN models. Concretely, this paper first presents a general interpretation method called high-order class activation mapping (HO-CAM), which leverages the internal structure of tensor-based CNN with global average pooling (GAP) and generate explanations by highlighting attention regions of prediction result. Then, to apply this high-order interpretation method to tensor-based CNNs with any structures, two advanced interpretation methods, namely high-order gradient-weighted class activation mapping (HOGCAM) and high-order layer class activation mapping (HOLCAM), are proposed by utilizing gradient information with two different weighting strategies. Experimental results demonstrate that HO-CAM performs best in explaining the last layer of specific tensor-based CNNs with GAP, HOG-CAM removes the reliance on GAP and is applicable to any forms of tensor-based CNNs, and HOL-CAM exhibits better interpretation performance than HOG-CAM across arbitrary layers of any models. © 2023 IEEE.

关键词： Tensors

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Style Transfer of Traditional Chinese Bamboo Flute Music based on AI Model 21

Style Transfer of Traditional Chinese Bamboo Flute Music Bas...

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作者： Qin, Ziqi Beijing National Day School China

ISBN: (纸本)9798350329223

The application of AI technology in music can promote the development of multi-source and diversity of the music style, which can effectively promote the exchange of music culture and the enrichment of the music market. In this paper, we proposed DNN-CycleGAN model to convert audio signals of any length from bamboo flute music features to flute music features. We use a short-term Fourier transform algorithm (STFT) to realize the time-frequency conversion of the waveform music, and then utilize a deep convolutional neural network structure (DNN) to extract the features of the waveform music in the Chinese bamboo flute. Then we structure the CycleGAN model to transfer the bamboo flute music into flute music. The experimental results show that the converted music has a precise rhythm, and the timbre characteristics of the flute and bamboo flute are demonstrated to a certain degree. © 2023 IEEE.

关键词： Music

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Energy-efficient Service Deployment based on Multi-Dimensional Features in Mobile Edge Computing: A Learning-based Approach

Energy-efficient Service Deployment Based on Multi-Dimension...

引用

2024 IEEE Global Communications conference, GLOBECOM 2024

作者： Chen, Xiangyi Li, Yang Xing, Huanlai Zheng, Danyang Li, Min Xu, Lexi Zhao, Hai Southwest Jiaotong University School of Computing and Artificial Intelligence Chengdu611756 China China United Network Communications Beijing100048 China Northeastern University School of Computer Science and Engineering Shenyang110819 China

ISBN: (纸本)9798350351255

Mobile edge computing (MEC) decentralizes the computational and storage capabilities of the network to edge nodes, providing support for the dynamic deployment and rapid response of mobile services. However, the large-scale distributed deployment of edge nodes, their widespread geographic distribution, multi-dimensional and complexly dependent service characteristics, and the dynamically changing network environment pose challenges to energy-efficient service deployment. In this paper, we consider multi-dimensional features for service deployment, including dynamic traffic demand, geography information, service semantics, and service popularity, with the aim of improving the availability of edge services and reducing network energy consumption. Firstly, to handle the large volume of edge service data, we design a multi-dimensional feature extraction approach based on the Transformer model, which does not rely on the sequential order of data and can enhance computational efficiency of edge models through parallel processing. Then, to adapt to the dynamically changing edge network environment, we propose an Energy-Efficient Service Deployment algorithm (EESD) based on the improved Dueling Deep Q-network, which makes service deployment and base station switching decisions in a learning-based manner. Finally, simulation results demonstrate that EESD outperforms comparison algorithms in terms of model convergence, system total cost, and energy consumption. © 2024 IEEE.

关键词： Mobile edge computing

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A Novel Robust Reinforcement Learning-based Dependent Task Offloading Algorithm for Mobile Edge Intelligence 29

A Novel Robust Reinforcement Learning-based Dependent Task O...

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29th IEEE International conference on parallel and distributed Systems, ICPADS 2023

作者： Deng, Xu Sun, Peng Yang, Kun Fang, Gaoyun Boukerche, Azzedine Song, Liang Fudan University Academy for Engineering and Technology China Innovation Platform for Academicians of Hainan Province Haikou570228 China Duke Kunshan University China University of Ottawa Canada

ISBN: (纸本)9798350330717

With the rise of advanced applications based on Artificial Intelligence (AI) and Internet-of-Things (IoT), mobile devices have become more intelligent, introducing a novel concept, Mobile Edge Intelligence. But the limited on-board resources often hinder the capabilities of mobile devices. Mobile Edge Computing (MEC), regarded as an effective method to expand device capability, effectively overcomes this barrier. However, the dynamic networks driven by mobility and the dependency on applications pose significant challenges for offloading, which can degrade MEC's overall performance. Therefore, how to effectively combine the above points to achieve a stable and effective sharing of computing resources between devices and servers is a critical issue. In this paper, we consider a multi-slot MEC system with device mobility and multiple applications of unknown arrival. To improve application completion rate while reducing task delay, we introduce a novel, robust distributed offloading algorithm, which calls the Multi-Attention Pointer network-based Reinforcement Learning algorithm (MAPRL), for the dynamic and unstable resource offloading scenario. Numerous experiments have been carried out to demonstrate that, compared with the existing methods, MAPRL exhibits robustness when facing the changing scenario, it can adapt to the unknown workload and dynamic network connections to enhance the offloading performance. © 2023 IEEE.

关键词： deep reinforcement learning dependent task offloading Mobile edge computing

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