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作者： Tang, Wenda Wu, Chaobing Qi, Lianyong Zhang, Xuyun Xu, Xiaolong Dou, Wanchun State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Department of Computer Science and Technology Nanjing University Nanjing China School of Information Science and Engineering Chinese Academy of Education Big Data Qufu Normal University Rizhao China Department of Electrical and Computer Engineering The University of Auckland Auckland New Zealand Jiangsu Engineering Centre of Network Monitoring School of Computer and Software Nanjing University of Information Science and Technology Nanjing China

With an increasing number of public WiFi hotspots have been constructed in metropolitan areas, citizens can leverage these WiFi hotspots to surf the Internet with their smart devices almost everywhere. Compared to cellular networks, most people prefer using WiFi because this can not only save money by reducing the cellular data traffic but also save energy to prolong the battery lifetime of mobile devices. Mobile offloading techniques give the opportunity for smart devices to offload cellular data traffic to WiFi networks whenever they are connected WiFi networks. Although offloading to WiFi networks can cause communication delay, many delay-tolerant applications still prefer doing so in order to save the cellular data traffic. Existing offloading approaches only take the application delay tolerance into consideration and therefore easy to induce some tasks missing deadlines. In this paper, we propose a novel WiFi-aware method for mobile data offloading through WiFi networks with deadline constraints. Specifically, it takes the WiFi distribution in city areas into consideration while making offloading decision. The simulation results on real-world data have shown that our method improves the percentage of tasks that can meet their deadlines, and reduces the average task completion time by comparing with other outstanding methods. © 2019 John Wiley & Sons, Ltd.

关键词： Wireless local area networks (WLAN)

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Encryption Traffic Classification Based on Mining Traffic Context and Transport Relationship

Encryption Traffic Classification Based on Mining Traffic Co...

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2025 IEEE Wireless Communications and Networking Conference, WCNC 2025

作者： Gai, Weilin Zhang, Runqing Zhang, Huiyuan Guo, Yu Yin, Jun Zhang, Peng Tca Institute of Software Chinese Academy of Sciences Beijing China School of Computer Science and Technology University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response TechnicalTeam Coordination Center of China Beijing China School of Cyber Science and Engineering Nanjing University of Science and Technology Nanjing China

ISBN: (纸本)9798350368369

This paper proposes a novel ETC-MTCTR, which is designed to enable more accurate, versatile and efficient traffic classification in the context of multi-scenario, low-resource encrypted traffic. Through three modules of Datagram Token conversion, pretraining and fine-tuning, the method uses large-scale unlabeled encrypted traffic for pretraining, mining and learning the traffic context and transmission relationship of encrypted traffic classification tasks, so that a small number of labeled data samples can be effectively used in the fine-tuning stage. Significantly improve the performance of the model on specific downstream classification tasks, enhance the accuracy, adaptability and robustness of the model in diverse environments, limited resources and new encryption security protocols, and realize efficient encryption traffic classification in multi-scenario and low-resource background. The results show that ETC-MTCTR achieves the best performance on three tasks: encryption malware classification, VPN encrypted traffic classification, and TLS 1.3 encryption application classification. Its F1 score is improved by 0.22% in the classification task of encrypted malware, 1.4% in the classification task of VPN encrypted traffic App, 4.56% in the classification task of VPN encrypted traffic Service, and 9.89% in the classification task of TLS 1.3 encrypted application, which is significantly better than other comparison methods. © 2025 IEEE.

关键词： Traffic control

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Correlation Fusion with Instance-level Auto-weighted for Multi-view Graph Clustering

Correlation Fusion with Instance-level Auto-weighted for Mul...

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IEEE International Conference on High Performance Computing and Communications (HPCC)

作者： Xuan Chen Zhikui Chen Liang Yao Shubin Ma Peng Li Shuo Yu School of Software Dalian University of Technology Da'lian China School of Electrical Engineering Anhui Polytechnic University Wu'hu China School of Computer Science and Technology Dalian University of Technology Da'lian China

Multi-view graph clustering (MGC) has emerged as a hot research topic due to its effectiveness. Previous works mainly focus on the fusion of similarity graphs to exploit the complementary and consistent. However, these approaches suffer from three shortcomings: 1) The distance similarity estimate by the linear Euclidean metric may be incorrect in revealing the association relationship of heterogeneous multi-view samples. 2) Most of them often weight the whole view to balance the effect of different views, which ignores the importance of local samples. 3) They fail to preserve the inherent manifold structure between views via directly applying similarity matrix fusion to obtain a consistent graph. To fill these gaps, this paper proposed a novel framework called correlation fusion with instance-level auto-weighted for MGC (CFIMGC). In particular, the Spearman distance is leveraged to effectively characterize the versatile correlation matrix. The sample-level auto-weighted fusion mechanism is presented for flexible assigning weight values to different sample-wise relations in intra-view and inter-view, through which the complementary of numerous correlation matrices is appropri-ately explored to produce a consistent correlation matrix. What's more, a probability graph is adaptively learned based on the fusion correlation matrix for constructing a clustering-friendly similarity graph. Meanwhile, an alternating iteration algorithm is devised to address the non-convex joint optimization problem. Comprehensive experiment results on seven widely used but challenging multi-view datasets demonstrate that the proposed method yields superiority in clustering performance against six state-of-the-art counterparts.

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Adaptive Coordination with Graph Convolution Networks and Grey Wolf Optimization for Next-Gen 5G Network Slicing

Adaptive Coordination with Graph Convolution Networks and Gr...

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International Conference on Fuzzy Theory and it's Applications (iFUZZY)

作者： M. Kowsalya In-Ho Ra K. S. Naveenraj S. Thruvenkadam Seok-Woo Park School of Electrical Engineering Vellore Institute of Technology Vellore India School of Computer & Software Kunsan National University Gunsan South Korea Internet Protocol Division Jio Platform Limited Mumbai India

ISBN: (数字)9798350352788

ISBN: (纸本)9798350352795

The need for flexible and effective 5G communication networks has prompted studies into novel approaches for network slicing, a fundamental paradigm that permits network service personalization for a range of applications. In this research, we provide a unique technique that combines Graph Convolution Networks (GCN) and Grey Wolf Optimization (GWO) strategies to improve the adaptive configuration of 5G network slices. GCN, a software capable of recording detailed network interactions, and GWO, well-known for its metaheuristic optimization, combine to offer a strong foundation for tackling dynamic network slicing challenges. We optimize GWO settings via rigorous testing and demonstrate the effectiveness of the integrated strategy by achieving gains in important performance measures including latency, throughput, and resource use. The investigation not only advances the continuing development of 5G networks but also highlights how cutting-edge machine learning and optimization techniques can completely transform network slicing approaches.

关键词： Adaptive systems 5G mobile communication Convolution Network slicing Transforms Throughput software

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Addressing Asynchronicity in Clinical Multimodal Fusion via Individualized Chest X-ray Generation

arXiv

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arXiv 2024年

作者： Yao, Wenfang Liu, Chen Yin, Kejing Cheung, William K. Qin, Jing School of Nursing The Hong Kong Polytechnic University Hong Kong Department of Computer Science Hong Kong Baptist University Hong Kong School of Software Engineering South China University of Technology China

Integrating multi-modal clinical data, such as electronic health records (EHR) and chest X-ray images (CXR), is particularly beneficial for clinical prediction tasks. However, in a temporal setting, multi-modal data are often inherently asynchronous. EHR can be continuously collected but CXR is generally taken with a much longer interval due to its high cost and radiation dose. When clinical prediction is needed, the last available CXR image might have been outdated, leading to suboptimal predictions. To address this challenge, we propose DDL-CXR, a method that dynamically generates an up-to-date latent representation of the individualized CXR images. Our approach leverages latent diffusion models for patient-specific generation strategically conditioned on a previous CXR image and EHR time series, providing information regarding anatomical structures and disease progressions, respectively. In this way, the interaction across modalities could be better captured by the latent CXR generation process, ultimately improving the prediction performance. Experiments using MIMIC datasets show that the proposed model could effectively address asynchronicity in multimodal fusion and consistently outperform existing methods. © 2024, CC BY-NC-SA.

关键词： Electronic health record

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FedAdOb: Privacy-Preserving Federated Deep Learning with Adaptive Obfuscation

arXiv

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arXiv 2024年

作者： Gu, Hanlin Luo, Jiahuan Kang, Yan Yao, Yuan Zhu, Gongxi Li, Bowen Fan, Lixin Yang, Qiang WeBank AI Lab WeBank China The Department of math Hong Kong University of Science and Technology Hong Kong The School of Information and Software Engineering University of Electronic Science and Technology of China Chengdu China The Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai200240 China The Department of Computer Science and Engineering Hong Kong University of Science and Technology Hong Kong and WeBank AI Lab WeBank China

Federated learning (FL) has emerged as a collaborative approach that allows multiple clients to jointly learn a machine learning model without sharing their private data. The concern about privacy leakage, albeit demonstrated under specific conditions [1], has triggered numerous follow-up research in designing powerful attacking methods and effective defending mechanisms aiming to thwart these attacking methods. Nevertheless, privacy-preserving mechanisms employed in these defending methods invariably lead to compromised model performances due to a fixed obfuscation applied to private data or gradients. In this article, we, therefore, propose a novel adaptive obfuscation mechanism, coined FedAdOb, to protect private data without yielding original model performances. Technically, FedAdOb utilizes passport-based adaptive obfuscation to ensure data privacy in both horizontal and vertical federated learning settings. The privacy-preserving capabilities of FedAdOb, specifically with regard to private features and labels, are theoretically proven through Theorems 1 and 2. Furthermore, extensive experimental evaluations conducted on various datasets and network architectures demonstrate the effectiveness of FedAdOb by manifesting its superior trade-off between privacy preservation and model performance, surpassing existing methods. Copyright © 2024, The Authors. All rights reserved.

关键词： Network architecture

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Dynamic resource provisioning for workflow scheduling under uncertainty in edge computing environment

Dynamic resource provisioning for workflow scheduling under ...

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作者： Xu, Xiaolong Cao, Hao Geng, Qingfan Liu, Xihua Dai, Fei Wang, Chuanjian School of Computer and Software Nanjing University of Information Science and Technology Nanjing China Jiangsu Engineering Center of Network Monitoring Nanjing University of Information Science and Technology Nanjing China Nanjing University of Information Science and Technology Nanjing China Engineering Research Center of Digital Forensics Ministry of Education Nanjing China School of Big Data and Intelligence Engineering Southwest Forestry University Kunming China College of Information Science and Technology Shihezi University Xinjiang China

Edge computing, an extension of cloud computing, is introduced to provide sufficient computing and storage resources for mobile devices. Moreover, a series of computing tasks in a mobile device are set as structured computing processes and flows to achieve effective management by the workflow. However, the execution uncertainty caused by performance degradation, service failure, and new service additions remains a huge challenge to the user's service experience. In order to address the uncertainty, a software-defined network (SDN)-based edge computing framework and a dynamic resource provisioning (UARP) method are proposed in this paper. The UARP method is implemented in the proposed framework and addresses the uncertainty through the advantages of SDN. In addition, the nondominated sorting genetic algorithm-III is employed to optimize two goals, that is, the energy consumption and the completion time, to obtain balanced scheduling strategies. The comparative experiments are performed and the results show that the UARP method is superior to other methods in addressing the uncertainty, while reducing energy consumption and shortening the completion time. © 2020 John Wiley & Sons, Ltd.

关键词： Edge computing

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A Novel Attitude Estimation Algorithm Based on EKF-LSTM Fusion Model 5th

A Novel Attitude Estimation Algorithm Based on EKF-LSTM Fusi...

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5th International Conference on Cognitive Systems and Signal Processing, ICCSIP 2020

作者： Zhuo, Yufan Sun, Fuchun Wen, Zhenkun Wu, Huisi Huang, Haiming College of Electronics and Information Engineering Shenzhen University Shenzhen518060 China Department of Computer Science and Technology Tsinghua University Beijing100083 China College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China

ISBN: (纸本)9789811623356

The application of the low-cost inertial measurement unit (IMU) in many fields is growing, but the related attitude algorithms have the problems of low precision and poor adaptability. In this paper, a novel attitude estimation algorithm based on the fusion model of extend Kalman filter (EKF) and long short-term memory (LSTM) is proposed, which is composed of two main process: the initial attitude estimation of EKF and the subsequent calibration of LSTM. In this algorithm, EKF estimates the target’s attitude angles by the inputs of sensor data from IMU, then LSTM makes a calibration of each axis’ estimated angles, which is weighted with KEF’s result to export the optimal estimation finally. The result of simulation experiment shows that this algorithm is 50.515% lower on average than EKF under different working conditions when using mean squared error (MSE) as the evaluation indicator, which could be concluded that this novel algorithm performs better than EKF, and provides a new way for attitude estimation. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Long short-term memory

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Survey on Image Object Detection Algorithms Based on Deep Learning 7th

Survey on Image Object Detection Algorithms Based on Deep Le...

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7th International Conference on Artificial Intelligence and Security, ICAIS 2021

作者： Fang, Wei Shen, Liang Chen, Yupeng School of Computer and Software Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science and Technology Nanjing China Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou China

ISBN: (纸本)9783030786083

With the development of image processing technology, computer vision is becoming more and more popular. In recent years, deep learning has flourished, significant progress has been made in object detection. Especially after the R-CNN framework was proposed, the object detection framework based on deep learning has gradually become the mainstream, which can be divided into two categories: region-based and regression-based. Taking these two types of frameworks as the main body, this paper summarizes the research background and then discusses the object detection algorithms based on candidate regions represented by Faster R-CNN and the algorithms based on regression represented by the YOLO series. According to the development history, this paper summarizes the framework proposed in recent years, compares and analyzes the performance of object detection algorithms on public datasets, and introduces the application scenarios of those algorithms. Finally, this paper discusses the current difficulties and challenges in object detection and looks forward to the future development direction. © 2021, Springer Nature Switzerland AG.

关键词： Object detection

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Collaborative Ground-Space Communications via Evolutionary Multi-objective Deep Reinforcement Learning

arXiv

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arXiv 2024年

作者： Li, Jiahui Sun, Geng Wu, Qingqing Niyato, Dusit Kang, Jiawen Jamalipour, Abbas Leung, Victor C.M. The College of Computer Science and Technology Jilin University Changchun130012 China The School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore The Department of Electronic Engineering Shanghai Jiao Tong University Shanghai200240 China The School of Automation Guangdong University of Technology Guangzhou510006 China The School of Electrical and Computer Engineering The University of Sydney SydneyNSW2006 Australia The College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China The Department of Electrical and Computer Engineering The University of British Columbia VancouverV6T 1Z4 Canada

In this paper, we propose a distributed collaborative beamforming (DCB)-based uplink communication paradigm for enabling ground-space direct communications. Specifically, DCB treats the terminals that are unable to establish efficient direct connections with the low Earth orbit (LEO) satellites as distributed antennas, forming a virtual antenna array to enhance the terminal-to-satellite uplink achievable rates and durations. However, such systems need multiple trade-off policies that variously balance the terminal-satellite uplink achievable rate, energy consumption of terminals, and satellite switching frequency to satisfy the scenario requirement changes. Thus, we perform a multi-objective optimization analysis and formulate a long-term optimization problem. To address availability in different terminal cluster scales, we reformulate this problem into an action space-reduced and universal multi-objective Markov decision process. Then, we propose an evolutionary multi-objective deep reinforcement learning algorithm to obtain the desirable policies, in which the low-value actions are masked to speed up the training process. As such, the applicability of a onetime trained model can cover more changing terminal-satellite uplink scenarios. Simulation results show that the proposed algorithm outmatches various baselines, and draw some useful insights. Specifically, it is found that DCB enables terminals that cannot reach the uplink achievable threshold to achieve efficient direct uplink transmission, which thus reveals that DCB is an effective solution for enabling direct ground-space communications. Moreover, it reveals that the proposed algorithm achieves multiple policies favoring different objectives and achieving near-optimal uplink achievable rates with low switching frequency. Copyright © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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