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IEEE International Conference on Trust, Security and Privacy in computing and Communications (TrustCom)

作者： Weiqi Dai Zhenhui Lu Xia Xie Duoqiang Wang Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China School of Computer Science and Technology Hainan University Haikou China

Medical data sharing can improve the quality of medical care and promote progress in the field of public health. However, medical data has high confidentiality and complexity. Taking the clinical diagnosis and treatment data of Diabetes Mellitus Type 2 (T2DM) in medical treatment as an example, T2DM data often has the phenomenon of "multi-disease coexistence" and is insecure and inaccurate in the process of sharing. Based on Blockchain and Attribute- Encryption (ABE), we propose a T2DM data sharing system to solve the above problems. An Attribute-Encryption algorithm T2DM-CP-ABE is proposed to solve the problem of excessive sharing granularity. T2DM-CP-ABE, combined with the medical data contribution module, takes the contribution factor as an important consideration, to stimulate the power of T2DM data sharing and promote the development of national diabetes prevention. Based on the Blockchain, a medical data attribute authorization model is proposed, which provides a credible solution for multiple hospitals to realize medical data attribute authorization, and realizes the clinical diagnosis and treatment data sharing of T2DM among doctors. Based on Blockchain and ABE technology, a data sharing system for T2DM has been implemented on the Fabric platform. The result shows that the average time for the system to complete a single sharing is less than 1 second.

关键词： Authorization Hospitals Fabrics Blockchains Diabetes Encryption Security

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MAST: Video Polyp Segmentation with a Mixture-Attention Siamese Transformer

arXiv

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arXiv 2024年

作者： Chen, Geng Yang, Junqing Pu, Xiaozhou Ji, Ge-Peng Xiong, Huan Pan, Yongsheng Cui, Hengfei Xia, Yong National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an China School of Computing Australian National University Canberra Australia Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates Harbin Institute of Technology China

Accurate segmentation of polyps from colonoscopy videos is of great significance to polyp treatment and early prevention of colorectal cancer. However, it is challenging due to the difficulties associated with modelling long-range spatio-temporal relationships within a colonoscopy video. In this paper, we address this challenging task with a novel Mixture-Attention Siamese Transformer (MAST), which explicitly models the long-range spatio-temporal relationships with a mixture-attention mechanism for accurate polyp segmentation. Specifically, we first construct a Siamese transformer architecture to jointly encode paired video frames for their feature representations. We then design a mixture-attention module to exploit the intra-frame and inter-frame correlations, enhancing the features with rich spatio-temporal relationships. Finally, the enhanced features are fed to two parallel decoders for predicting the segmentation maps. To the best of our knowledge, our MAST is the first transformer model dedicated to video polyp segmentation. Extensive experiments on the large-scale SUN-SEG benchmark demonstrate the superior performance of MAST in comparison with the cutting-edge competitors. Our code is publicly available at https://***/Junqing-Yang/MAST. Copyright © 2024, The Authors. All rights reserved.

关键词： Benchmarking

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DOANet: Point Cloud Registration with Deep Overlap Attention

DOANet: Point Cloud Registration with Deep Overlap Attention

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2021 China Automation Congress, CAC 2021

作者： Guan, Haining Gao, Qing Zhang, Baochang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Big Data and Brain Computing Beihang University Beijing100191 China

ISBN: (纸本)9781665426473

Pairwise point cloud registration is a fundamental task in 3D computer vision and various approaches have been proposed to handle this problem in recent years. Despite the fast evolution of registration algorithms, little attention has been drawn to align point cloud pairs with a low-overlap ratio (≤30%). In this paper, we propose a new neural network architecture named the DOANet, an end-to-end trainable fully convolutional neural network, to deal with the problem of registration with low overlap. In particular, we leverage sparse convolutions to extract robust and discriminative dense features, and further utilize attention mechanisms to explicitly predict the overlap region for point cloud fragments. The receptive field of our local point patches is constrained within the overlap region generated on the fly, which prevents the local point features from being contaminated by geometric information outside the overlap region. Experiments indicate the effectiveness and advantages of our method on both 3DLoMatch (all pairs under 30% overlap-ratio) and 3DMatch and illustrate obvious improvement over the existing results. © 2021 IEEE

关键词： Convolution

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Neural TTS-Based Dynamic data Augmentation for Improved Speech Separation

IEEE Transactions on Audio, Speech and Language Processing

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IEEE Transactions on Audio, Speech and Language Processing 2025年 33卷 2457-2470页

作者： Kai Wang Cuicui Zhu Lili Yin Sheng Li Madina Mansurova Hao Huang School of Computer Science and Technology Xinjiang University Urumqi China Joint International Research Laboratory of Silk Road Multilingual Cognitive Computing Urumqi China Xinjiang Key Laboratory of Multilingual Information Technology Urumqi China School of Engineering Institute of Science Tokyo Kanagawa Japan Department of Artificial Intelligence and Big Data Al-Farabi Kazakh National University Almaty Kazakhstan

Text-to-speech (TTS) synthetic data augmentation has been widely used in various speech processing tasks, but its effectiveness in speech separation remains understudied. In this paper, we present SpeakerAugment+ (SA+), a neural TTS-based dynamic data augmentation framework for speech separation. The SA+ framework consists of two modules: speaker module, which learns a Gaussian Mixture Model (GMM) to characterize the distribution over speaker embeddings in training data and samples unseen speaker embeddings during inference; speech module, which conditions speech synthesis using speaker embeddings with controllable speaker parameters. SA+ incorporates three augmentation techniques: speaker generation, parameter manipulation and utterance generation, enhancing speaker and utterance diversity from different perspectives. Following the SA+ framework, we design FS2-SA+ and Matcha-SA+, which are based on FastSpeech 2 and Matcha-TTS, respectively. We evaluate SA+ across multiple separation models and datasets, and the results demonstrate a substantial improvement in speech separation performance. Matcha-SA+ generates higher-quality speech and achieves better separation performance in intra-corpus tests. Conversely, FS2-SA+ supports a broad range of speaker parameter adjustments, leading to better generalization. Besides, the relationship between harmonicity and speech separation is a widely researched topic, and our findings indicate that speech lacking an explicit harmonic structure, when generated by neural TTS, can function as augmented data to improve speech separation. This research underscores the effectiveness of neural TTS-based data augmentation in speech separation tasks. We hope that our work can offer insights for future studies investigating data augmentation strategies within speech separation.

关键词： data augmentation Training Speech synthesis Speech recognition Speech processing Training data Vocoders Speech enhancement Harmonic analysis Optimization

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TA-MoE: Topology-Aware Large Scale Mixture-of-Expert Training

arXiv

引用

arXiv 2023年

作者： Chen, Chang Li, Min Wu, Zhihua Yu, Dianhai Yang, Chao Center for Data Science Peking University China School of Mathematical Sciences Peking University China National Engineering Laboratory for Big Data Analysis and Applications Peking University China Baidu Inc China Institute for Computing and Digital Economy Peking University China

Sparsely gated Mixture-of-Expert (MoE) has demonstrated its effectiveness in scaling up deep neural networks to an extreme scale. Despite that numerous efforts have been made to improve the performance of MoE from the model design or system optimization perspective, existing MoE dispatch patterns are still not able to fully exploit the underlying heterogeneous network environments. In this paper, we propose TA-MoE, a topology-aware routing strategy for large-scale MoE trainging, from a model-system co-design perspective, which can dynamically adjust the MoE dispatch pattern according to the network topology. Based on communication modeling, we abstract the dispatch problem into an optimization objective and obtain the approximate dispatch pattern under different topologies. On top of that, we design a topology-aware auxiliary loss, which can adaptively route the data to fit in the underlying topology without sacrificing the model accuracy. Experiments show that TA-MoE can substantially outperform its counterparts on various hardware and model configurations, with roughly 1.01x-1.61x, 1.01x-4.77x, 1.25x-1.54x improvements over the popular DeepSpeed-MoE, FastMoE and FasterMoE systems. Copyright © 2023, The Authors. All rights reserved.

关键词： Network topology

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Network Situation Awareness Model Based on Incomplete Information Game 5th

Network Situation Awareness Model Based on Incomplete Inform...

引用

5th International Conference on Security and Privacy in New computing Environments, SPNCE 2022

作者： Zhang, Hongbin Yin, Yan Zhao, Dongmei Liu, Bin Wang, Yanxia Liu, Zhen School of Information Science and Engineering Hebei University of Science and Technology Shijiazhuang050000 China Hebei Key Laboratory of Network and Information Security Hebei Normal University Hebei Shijiazhuang050024 China School of Economics and Management Hebei University of Science and Technology Shijiazhuang050000 China Research Center of Big Data and Social Computing Hebei University of Science and Technology Shijiazhuang050000 China Hebei Geological Workers’ University Shijiazhuang050081 China

ISBN: (纸本)9783031306228

Game theory has been widely used in network security situational awareness. However, most of the currently proposed game-based offensive and defensive situational awareness methods are for traffic data, and there are fewer models or methods for analysis using vulnerability data. To overcome these issues, this paper proposes collecting periodic security vulnerability information in the network and utilizing the change in vulnerability status to achieve network security situational awareness. At this time, a network attack and defense game model based on incomplete information is proposed, which uses the state changes of the vulnerability life cycle to model the attack and defense behavior, calculates the benefits of both attack and defense through the evaluation of the exploitability of the vulnerability, and then quantifies the security situation value. We carried out the experiments using the vulnerability dataset, which was obtained by scanning the IP addresses of several enterprises in Hebei Province, China. The experimental results show that the approach of using network security vulnerabilities to assess network security status is feasible. © 2023, ICST Institute for Computer Sciences, Social Informatics and Telecommunications engineering.

关键词： Network security

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An autoencoder-like nonnegative matrix co-factorization for improved student cognitive modeling 24

An autoencoder-like nonnegative matrix co-factorization for ...

引用

Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Shenbao Yu Yinghui Pan Yifeng Zeng Prashant Doshi Guoquan Liu Kim-Leng Poh Mingwei Lin College of Computer and Cyber Security Fujian Normal University China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University China Department of Computer and Information Sciences Northumbria University UK Intelligent Thought and Action Lab School of Computing University of Georgia Financial Technology Research Institute Fudan University China College of Design and Engineering National University of Singapore Singapore

ISBN: (纸本)9798331314385

Student cognitive modeling (SCM) is a fundamental task in intelligent education, with applications ranging from personalized learning to educational resource allocation. By exploiting students' response logs, SCM aims to predict their exercise performance as well as estimate knowledge proficiency in a subject. data mining approaches such as matrix factorization can obtain high accuracy in predicting student performance on exercises, but the knowledge proficiency is unknown or poorly estimated. The situation is further exacerbated if only sparse interactions exist between exercises and students (or knowledge concepts). To solve this dilemma, we root monotonicity (a fundamental psychometric theory on educational assessments) in a co-factorization framework and present an autoencoder-like nonnegative matrix co-factorization (AE-NMCF), which improves the accuracy of estimating the student's knowledge proficiency via an encoder-decoder learning pipeline. The resulting estimation problem is nonconvex with nonnegative constraints. We introduce a projected gradient method based on block coordinate descent with Lipschitz constants and guarantee the method's theoretical convergence. Experiments on several real-world data sets demonstrate the efficacy of our approach in terms of both performance prediction accuracy and knowledge estimation ability, when compared with existing student cognitive models.

关键词：

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Federated-Learning-Based Wireless Traffic Prediction

Federated-Learning-Based Wireless Traffic Prediction

引用

International Conference on Artificial Intelligence in Information and Communication (ICAIIC)

作者： Feng Li Xiaona Chen Kwok-Yan Lam Bowen Shen Li Wang School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Strategic Centre for Research in Privacy-Preserving Technologies & Systems Nanyang Technological University Singapore College of Computing and Data Science Nanyang Technological University Singapore College of Marine Electrical Engineering Dalian Maritime University Dalian China

ISBN: (数字)9798331507022

ISBN: (纸本)9798331507039

Accurate traffic prediction is crucial for enhancing the performance of intelligent cellular networks, as it directly impacts the effective allocation of network resources and user satisfaction. The instability and dynamics of traffic data pose challenges to centralized prediction methods. These methods are limited in prediction accuracy and may lead to issues concerning data privacy breaches and response delays. Furthermore, traditional Federated Learning (FL) typically employs a simple averaging strategy during model aggregation, which does not consider the heterogeneity of client data, thereby affecting the generalization performance of the global model. Therefore, this study proposes a wireless traffic prediction model that combines FL, Long Short-Term Memory (LSTM), and Kolmogorov-Arnold Networks (KAN). The aim is to enhance prediction accuracy while ensuring data privacy. The model leverages edge computing and FL techniques, enabling multiple edge devices to train LSTM models locally. By integrating KAN to explore the inherent complexity of traffic data, it enhances the model's adaptability. Furthermore, the FedNova model is introduced, which thoroughly considers the heterogeneity of data among devices and optimizes the process of updating model parameters. The extensive experimental results conducted on the Milano real dataset demonstrate that the FedNova-LSTM-KAN model exhibits significant advantages compared to existing centralized and FL methods when dealing with uneven client traffic data.

关键词： Wireless communication Adaptation models data privacy Accuracy Federated learning Computational modeling Predictive models Prediction algorithms data models Long short term memory

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DQPS: An Intelligent Multi-Hop Computation Offloading Scheme for Workflow Applications in Vehicular Edge computing Networks

引用

IEEE Transactions on Consumer Electronics 2024年第1期71卷 2202-2216页

作者： Lin, Bing Chen, Qiaoxin Chen, Xing Jia, Wen-Kang Lu, Yu Xiong, Neal N. Fujian Normal University College of Physics and Energy Fujian Provincial Key Laboratory of Quantum Manipulation and New Energy Materials Fuzhou350117 China Fujian Provincial Collaborative Innovation Center for Advanced High-Field Superconducting Materials and Engineering Fuzhou350117 China Peking University School of Computer Science 100871 China Xiamen University Department of Informatics and Communication Engineering Xiamen361005 China Fuzhou University College of Computer and Data Science Fuzhou350118 China Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou350118 China Fujian Normal University College of Photonic and Electronic Engineering Fuzhou350117 China Fujian Normal University Concord University College Fuzhou350117 China Sul Ross State University Department of Computer Mathematical and Physical Sciences AlpineTX79830 United States

Vehicular Edge computing (VEC) is a feasible solution for autonomous driving as it can offload latency-sensitive and computation-intensive tasks from vehicle terminals to roadside units (RSUs) for real-time processing. Due to the high computational resource the workflow applications (i.e., autonomous driving) required, the small vehicle-to-RSUs communication range, and the scarce available resources for each vehicle, it might be difficult to accomplish complex workflow applications through the single-hop offloading paradigm in the Internet of Vehicles (IoV). In this paper, we propose a reinforcement learning (RL) based multi-hop computation offloading scheme for workflow applications to reduce their execution latency in the VEC networks, which considers the data dependency in workflow applications as well as the trust relationship and communication interference among RSUs. Firstly, a preprocessing method is employed to merge the cut-edges in a workflow to reduce the offloading scale of tasks and compress the encoding dimension of offloading solutions. Then, a Deep Q-network algorithm using Phase-optimal State update (DQPS) is proposed to update the offloading policy distribution, in which the deviation of the phase-optimal state from the current state is estimated as the reward of RL to promote the algorithm's convergence to adapt the dynamic VEC networks. Simulation results show that DQPS has the best performance compared to other benchmark schemes. Moreover, the latency of the identical applications can be reduced by 2.5%-25.3% through our offloading scheme with a multi-hop model compared to that with the single-hop paradigm in IoV. © 1975-2011 IEEE.

关键词： Computation offloading

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Hybrid Gradient Descent and Sea Lion Optimization Algorithm (H-GD-SLnO) to Optimize Task Scheduling in Fog computing Environment

Hybrid Gradient Descent and Sea Lion Optimization Algorithm ...

引用

Advanced computing Technologies (ICoACT), International Conference on

作者： Rakesh Premkumar N. Jagadish Kumar L Maria Michael visuwasam Gopalakrishnan Arjunan A. Vinothini C. Tharun Kumar School of informatics and computing Indiana University Bloomington USA Dept of Data Science and Business Systems School of Computing SRM Institute of Science and Technology Chennai Tamil Nadu India Dept of AI & DS RMK College of engineering and technology Puduvoyal Chennai Accenture Bangalore India Dept of CSE Vel Tech Multi Tech Dr. Rangarajan Dr. Sakunthala Engineering College Chennai Tamil Nadu India Dept of CSBS Sri Sai Ram Engineering college Chennai Tamil Nadu India

ISBN: (数字)9798331509859

ISBN: (纸本)9798331509866

The architectures associated with cloud and fog computing are fundamentally important for the decentralization of computational tasks, as they facilitate the efficient processing of various user demands while simultaneously supporting extensive Internet of Things (IoT) networks that are increasingly prevalent in today's technological landscape. As the complexity and scale of computational loads continue to expand at a rapid pace, it becomes imperative to implement effective task scheduling mechanisms across these heterogeneous architectures, as this is vital for the optimization of resource utilization and the significant reduction of latency that can adversely affect system performance. This Paper centers on optimization, highlighting the novel integration of Gradient Descent (GD) and the Sea Lion Optimization Algorithm (SLnO). It proposes a harmonious methodology for addressing task scheduling challenges in Fog computing by utilizing the accuracy of GD for localized searches and the exploratory strengths of SLnO for overarching optimization. The hybrid methodology (H-GD-SLnO) was evaluated using synthetic datasets and real grid workloads, including DAS-2 and AuverGrid, as well as Hadoop tasks like Terasort. The simulation outcomes reveal that (H-GD-SLnO) surpasses numerous pre-existing algorithms, realizing reductions in mean job completion time as well as average scheduling delay and enhancements in resource utilization and throughput. These results emphasize the efficacy of our proposed methodology in meeting the rigorous task scheduling demands within cloud and fog computing environments.

关键词： Cloud computing Processor scheduling Throughput Virtual machines Scheduling Resource management Internet of Things Marine animals Optimization Edge computing

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