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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Yu Ke Junli Wang Chungang Yan Jiamin Yao Key Laboratory of Embedded System and Service Computing Collaborative Innovation Center for Financial Network Security (Tongji University) Ministry of Education National (Province-Ministry Joint) Tongji University Shanghai China

With the expansion of network scale, traditional routing strategies in Software-Defined networking (SDN) have difficulty adapting to fast traffic variability. The large flow in the network occupies 80% of the network traffic. An unreasonable schedule of the large flow will lead to the emergence of bottleneck links and reduce the network performance. To solve the above problems, this paper proposes a different routing method, called Routing Strategy for SDN Large Flow Based on Deep Reinforcement Learning (RSDRL). Considering the links in the network that can carry large flow are limited, RSDRL can schedule large flow firstly and generate routing strategies adaptively. At the same time, in order to avoid the occurrence of congestion, RSDRL quantifies the degree of congestion in the network to implement routing. Experiments show that the proposed model has better Quality of Service (QoS) performance under the test traffic, realizing a reasonable schedule of traffic.

关键词： Deep learning Training Knowledge engineering Schedules Adaptation models Reinforcement learning Quality of service

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A Dual-stage Attention Based SDN Traffic Prediction Method

A Dual-stage Attention Based SDN Traffic Prediction Method

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IEEE International Conference on Big Data and Cloud computing (BdCloud)

作者： Chenjing Gao Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

Traffic matrix is the main research object of traffic prediction in software-defined networking. Accurate and timely traffic matrix prediction plays an important role in avoiding network congestion. While various methods have been proposed in previous studies to solve the SDN traffic prediction problem, few of them consider both inter- and intra-flow features. In this paper, we propose a dual-stage attention based Traffic Prediction method for the SDN traffic matrix prediction task. First, our method uses temporal pattern attention as the inter-flow attention mechanism before the encoding stage to realize adaptive feature extraction. Then, temporal attention is introduced as the intra-flow attention mechanism before the decoding stage to capture long-term temporal dependencies. Finally, we use the autoregressive module to handle the highly dynamic SDN traffic volume. Experimental results show that our proposed SDN traffic prediction method can capture more traffic features and improve the SDN traffic matrix prediction performance to some extent.

关键词： network topology Time series analysis Prediction methods Machine learning Feature extraction Graph neural networks Encoding

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LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-label Text Classification

LAMPT: LAbel Mask-Predicted Transformer for Extreme Multi-la...

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IEEE International Conference on Big Data

作者： Yifan Xu Minghao Zhu Junli Wang Chungang Yan Key Laboratory of Embedded System and Service Computing (Tongji University) Ministry of Education. National (Province-Ministry Joint) Collaborative Innovation Center for Financial Network Security Tongji University Shanghai China

ISBN: (纸本)9781665480468

Extreme Multi-label text Classification ( XMC) is a task of recalling the most relevant labels for each given text from an extremely large-scale label set. It is emphasized that XMC is a more complex classification task because there are two main problems: large space of labels and the labels in XMC tasks tend to be correlated. Existing methods attempt to model label correlations by viewing the XMC task as a sequence generation problem however they still suffer from (1) using the slow serial decoding strategy where labels are predicted one-by-one. (2) needing to compare a mass of label ordering strategies in the decoding stage to achieve satisfied accuracy. In this work, we propose LAbel Mask-Predicted Transformer (LAMPT) to address the both issues, which is a novel non-autoregressive generation model that (1) enriches the input raw text representation with the additional label features by fully exploiting the label dependencies, (2) allows for efficient parallel decoding thanks to its non-autoregressive decoding formulation and mask-prediced training strategy. Experimental results demonstrate that single model performance is substantially enhanced by LAMPT. On a Wiki dataset with thirty-one thousand labels, LAMPT-XLNet accuracy has gained 1.6% relative improvement on P@3 over the LightXML-XLNet. Also, the P@1 of ensemble LAMPT is 90.00%, a significant i mprovement over the state-of-the-art ensemble LightXML (transformer-based) and AttentionXML (LSTM-based), which achieve 89.45% and 87.47%, respectively.

关键词： Training Correlation Text categorization Information services Big Data Predictive models Transformers

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Energy allocation for activity recognition in wearable devices with kinetic energy harvesting

Energy allocation for activity recognition in wearable devic...

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作者： Xiao, Ling Meng, Yu Tian, Xiaobing Luo, Haibo College of Computer Science and Electronic Engineering Hunan University Changsha China College of Computer Science Guangdong University of Science And Technology Dongguan China Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University Changsha China College of Computer and Control Engineering Minjiang University Fujian China

Harvesting kinetic energy from body movement is regarded as a promising rechargeable energy source for wearable devices with low-power. Energy allocation is essential for motion-based rechargeable devices since the great variability of energy gained from movement. Based on the realistic characteristics of an ultra-low-power wearable devices and our measurement observations, we propose the optimization framework allocating energy to maximize the average accuracy of human activity recognition and provide an offline and online algorithm, respectively. We evaluate the proposed energy allocation approach with real-world human activity and kinetic energy harvesting datasets. Experimental results validate that our proposed energy allocation approach can maximize the energy allocation utility and improve energy efficiency of wearable devices. © 2021 John Wiley & Sons, Ltd.

关键词： Kinetic energy

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Effcient poisoning attacks and defenses for unlabeled data in DDoS prediction of intelligent transportation systems

Security and Safety

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Security and Safety 2022年第1期1卷 145-165页

作者： Zhong Li Xianke Wu Changjun Jiang College of Information Science and Technology Donghua UniversityShanghai 201620China Key Laboratory of the Ministry of Education for Embedded System and Service Computing Department of Computer ScienceTongji UniversityShanghai 201804China Shanghai Network Financial Security Collaborative Innovation Center Tongji UniversityShanghai 201804China

Nowadays,large numbers of smart sensors(e.g.,road-side cameras)which com-municate with nearby base stations could launch distributed denial of services(DDoS)attack storms in intelligent transportation *** attacks disable the services provided by base *** in this paper,considering the uneven communication traffic ows and privacy preserving,we give a hidden Markov model-based prediction model by utilizing the multi-step characteristic of DDoS with a federated learning framework to predict whether DDoS attacks will happen on base stations in the ***,in the federated learning,we need to consider the problem of poisoning attacks due to malicious *** poisoning attacks will lead to the intelligent transportation systems paralysis without security *** poisoning attacks mainly apply to the classi cation model with labeled *** this paper,we propose a reinforcement learning-based poisoningmethod speci cally for poisoning the prediction model with unlabeled ***,previous related defense strategies rely on validation datasets with labeled data in the ***,it is unrealistic since the local training datasets are not uploaded to the server due to privacy preserving,and our datasets are also ***,we give a validation dataset-free defense strategy based on Dempster-Shafer(D-S)evidence theory avoiding anomaly aggregation to obtain a robust global model for precise DDoS *** our experiments,we simulate 3000 points in combination with DARPA2000 dataset to carry out *** results indicate that our poisoning method can successfully poison the global prediction model with unlabeled data in a short ***,we compare our proposed defense algorithm with three popularly used defense *** results show that our defense method has a high accuracy rate of excluding poisoners and can obtain a high attack prediction probability.

关键词： Poisoning attacks Defenses Multi-step DDoS prediction Unlabeled data Intel-ligent transportation systems

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Monocular Camera-Based Visual Truck Type Recognition

SSRN

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SSRN 2024年

作者： Luo, Haibo Ye, Huaisheng Feng, Chao Shi, Xin Xie, Guoqi School of Computer and Data Science Minjiang University Fuzhou350018 China College of Computer and Data Science Fuzhou University Fuzhou350018 China College of Network and Information Security Fujian Agriculture and Forestry University Fuzhou350018 China Key Laboratory for Embedded and Network Computing of Hunan Province College of Computer Science and Electronic Engineering Hunan University Changsha410082 China College of Computer Science and Electronic Engineering Hunan University Changsha410082 China

Truck overload and over-limit are the primary causes of infrastructure damage and traffic safety accidents. In the past 2 years, researchers have started to deploy intelligent Internet of Things system at the source of truck delivery to enforce permit control. Truck type recognition is the core of such a system. However, existing methods suffer from limitations such as low recognition accuracy, high costs, difficult maintenance, and the need for road surface modifications. This study proposes a monocular camera-based visual truck type recognition method. First, the optimized lightweight deep neural network based on machine vision is utilized to detect truckaxles and recognize wheel hub categories. Next, a tracking algorithm and the video streamcontext information are employed to complete the robust statistics of the axle by neighborhoodcounting. Finally, the proposed method further achieves truck type recognition by leveragingavailable prior knowledge. Experimental results demonstrate that this method exhibits superiortime efficiency compared with existing algorithms, and it reduces average processing time by92.37% to 96.24% while ensuring high accuracy. Moreover, the method has been deployed onembedded devices with low computing power to enable real-time operation onsite. © 2024, The Authors. All rights reserved.

关键词： Object detection

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Software defined edge computing for distributed management and scalable control in IoT multinetworks

arXiv

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arXiv 2021年

作者： Wu, Di Nie, Xiang Deng, Hanhui Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China School of Electronic Engineering and Computer Science Queen Mary University of London United Kingdom

Recently, edge computing has emerged as a promising paradigm to support mobile access in IoT multinetworks. However, coexistence of heterogeneous wireless communication schemes brings about new challenges to the mobility management and access control in such networks. To maintain its computing continuum, Software-Defined networking (SDN) architecture can be applied to the distributed edge networks, which provides consistent frameworks for mobility management and creates secure mechanisms for access control. In this paper, we first review the state-of-the-art SDN technology and its applications in edge computing. In particular, we highlight the prominent issues related to mobility management and access control for SDN in the edge computing environment. We also introduce a number of effective solutions to these issues by presenting real-world testbed experiments, prototypes and numerical analysis of adopting SDN based edge computing paradigms for IoT multinetworks. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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STURE: Spatial-Temporal Mutual Representation Learning for Robust Data Association in Online Multi-Object Tracking

arXiv

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arXiv 2022年

作者： Wang, Haidong Li, Zhiyong Li, Yaping Nai, Ke Wen, Ming College of Computer Science Electronic Engineering of Hunan University Key Laboratory for Embedded and Network Computing of Hunan Province Hunan Province Changsha410082 China State Grid Hunan Electric Power Company Limited Economical Technical Research Institute Hunan Key Laboratory of Energy Internet Supply-demand and Operation Hunan Province Changsha410000 China

Online multi-object tracking (MOT) is a longstanding task for computer vision and intelligent vehicle platform. At present, the main paradigm is tracking-by-detection, and the main difficulty of this paradigm is how to associate current candidate detections with historical tracklets. However, in the MOT scenarios, each historical tracklet is composed of an object sequence, while each candidate detection is just a flat image, which lacks temporal features of the object sequence. The feature difference between current candidate detections and historical tracklets makes the object association much harder. Therefore, we propose a Spatial-Temporal Mutual Representation Learning (STURE) approach which learns spatial-temporal representations between current candidate detections and historical sequences in a mutual representation space. For historical trackelets, the detection learning network is forced to match the representations of sequence learning network in a mutual representation space. The proposed approach is capable of extracting more distinguishing detection and sequence representations by using various designed losses in object association. As a result, spatial-temporal feature is learned mutually to reinforce the current detection features, and the feature difference can be relieved. To prove the robustness of the STURE, it is applied to the public MOT challenge benchmarks and performs well compared with various state-of-the-art online MOT trackers based on identity-preserving metrics. Copyright © 2022, The Authors. All rights reserved.

关键词： E-learning

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Software-Defined Edge computing: A New Architecture Paradigm to Support IoT Data Analysis

arXiv

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arXiv 2021年

作者： Wu, Di Xie, Xiaofeng Nie, Xiang Fu, Bin Deng, Hanhui Zeng, Haibo Qin, Zhijin Key Laboratory for Embedded and Network Computing of Hunan Province Hunan University China Department of Electrical and Computer Engineering Virginia Tech United States EECS Queen Mary University of London United Kingdom

—The rapid deployment of Internet of Things (IoT) applications leads to massive data that need to be processed. These IoT applications have specific communication requirements on latency and bandwidth, and present new features on their generated data such as time-dependency. Therefore, it is desirable to reshape the current IoT architectures by exploring their inherent nature of communication and computing to support smart IoT data process and analysis. We introduce in this paper features of IoT data, trends of IoT network architectures, some problems in IoT data analysis, and their solutions. Specifically, we view that software-defined edge computing is a promising architecture to support the unique needs of IoT data analysis. We further present an experiment on data anomaly detection in this architecture, and the comparison between two architectures for ECG diagnosis. Results show that our method is effective and feasible. Copyright © 2021, The Authors. All rights reserved.

关键词： Edge computing

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Multi-Label Learning Based on Transfer Learning and Label Correlation

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Computers, Materials & Continua 2019年第7期61卷 155-169页

作者： Kehua Yang Chaowei She Wei Zhang Jiqing Yao Shaosong Long College of Computer Science and Electronic Engineering and Key Laboratory for Embedded and Network Computing of Hunan Province Hunan UniversityChangsha410082China Oath Verizon Company ManhattanNew York10007USA

In recent years,multi-label learning has received a lot of ***,most of the existing methods only consider global label correlation or local label *** fact,on the one hand,both global and local label correlations can appear in real-world situation at same *** the other hand,we should not be limited to pairwise labels while ignoring the high-order label *** this paper,we propose a novel and effective method called GLLCBN for multi-label ***,we obtain the global label correlation by exploiting label semantic ***,we analyze the pairwise labels in the label space of the data set to acquire the local ***,we build the original version of the label dependency model by global and local label *** that,we use graph theory,probability theory and Bayesian networks to eliminate redundant dependency structure in the initial version model,so as to get the optimal label dependent ***,we obtain the feature extraction model by adjusting the Inception V3 model of convolution neural network and combine it with the GLLCBN model to achieve the multi-label *** experimental results show that our proposed model has better performance than other multi-label learning methods in performance evaluating.

关键词： Bayesian networks multi-label learning global and local label correlations transfer learning

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