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IEEE Communications Society Conference on Sensor and Ad Hoc Communications and Networks (SECON)

作者： Tao Chang Li Li Meihan Wu Wei Yu Xiaodong Wang National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology China State Key Laboratory of Internet of Things for Smart City University of Macau China The 30th Research Institute of China Electronics Technology Group Corporation China

Federated Learning coordinates multiple devices to train a shared model while preserving data privacy. Despite its potential benefit, the increasing number of participating devices poses new challenges to the deployment in real-world cases. The highly limited amount of data located on each device coupled with significantly unbalanced data across different devices severely impede the performance of the shared model and the overall training progress at the same *** this paper, we propose FedHybrid, a hierarchical hybrid training framework for high-performance Federated Learning on a wide scale. Unlike the existing work that mainly focuses on the statistical challenge, FedHybrid establishes a hierarchical hybrid training framework that effectively utilizes the fragmented and unbalanced data located on the participating devices on a wide scale. Specifically, FedHybrid consists of the following two core components, a global coordinator deployed on the central server and a local coordinator deployed on each participating device. The global coordinator organizes the participating devices into different groups through jointly considering the system heterogeneity and unbalanced training data in order to accelerate the overall training progress while guaranteeing the model performance. Within each group, a novel device-to-device (D2D) sequential training procedure is coordinated by the local coordinator to effectively utilize the fragmented and unbalanced training data in order to intelligently update the local models. At the same time, we provide the theoretical analysis of FedHybrid and conduct extensive experiments to evaluate its effectiveness. The results show that FedHybrid effectively improves model accuracy up to 27% and accelerates the whole training process by 20% on average.

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A Physics and Data-Driven Hybrid PINNs Intelligent computing Method for Nuclear Engineering Simulation

A Physics and Data-Driven Hybrid PINNs Intelligent Computing...

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Electronic Information Engineering and Computer Science (EIECS), 2021 International Conference on

作者： Yufei Xie Wenlin Wang Guohua Wu Yang Yu Ping An Zibin Sun Haichuan Zhang Shengfeng Luo Yue Li School of Automation Wuhan University of Technology Wuhan China Sino-German College of Intelligent Manufacturing Shenzhen Technology University Shenzhen China Nuclear Power Institute of China Chengdu China National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology Changsha China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

In the field of nuclear energy, the Loss of Coolant Accident (LOCA) is recognized as one of the most severe types of nuclear reactor accidents, characterized by its complex physical processes and potentially catastrophic consequences. These challenges impose stringent requirements on safety analysis and emergency response. Accurate prediction and analysis of fluid behavior within pipelines under LOCA conditions are critical for evaluating accident outcomes and formulating response strategies. This paper introduces an innovative intelligent computing approach—a Physics-Informed Neural Networks (PINNs) model driven by both physical data and simulation data, specifically tailored for LOCA conditions. To address the challenges posed by multivariable and complex physical relationships, the six-equation two-fluid model is first simplified to represent the physical processes. Subsequently, a dual-driven PINNs network is developed, integrating both simulation data and physical constraints. The proposed model demonstrates a root mean square error (RMSE) of 0.02, a mean absolute error (MAE) of 0.044, and an R2 of 0.81 in predicting the outcomes under the six-equation model.

关键词： Solid modeling Accuracy Fluids Nuclear energy Computational modeling Pipelines Neural networks Predictive models Numerical models Accidents

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RoSAS: Deep Semi-supervised Anomaly Detection with Contamination-resilient Continuous Supervision

arXiv

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arXiv 2023年

作者： Xu, Hongzuo Wang, Yijie Pang, Guansong Jian, Songlei Liu, Ning Wang, Yongjun National Key Laboratory of Parallel and Distributed Computing China College of Computer National University of Defense Technology Hunan Changsha410073 China School of Computing and Information Systems Singapore Management University Singapore178902 Singapore College of Information and Communications National University of Defense Technology Hubei Wuhan430010 China

Semi-supervised anomaly detection methods leverage a few anomaly examples to yield drastically improved performance compared to unsupervised models. However, they still suffer from two limitations: 1) unlabeled anomalies (i.e., anomaly contamination) may mislead the learning process when all the unlabeled data are employed as inliers for model training;2) only discrete supervision information (such as binary or ordinal data labels) is exploited, which leads to suboptimal learning of anomaly scores that essentially take on a continuous distribution. Therefore, this paper proposes a novel semi-supervised anomaly detection method, which devises contamination-resilient continuous supervisory signals. Specifically, we propose a mass interpolation method to diffuse the abnormality of labeled anomalies, thereby creating new data samples labeled with continuous abnormal degrees. Meanwhile, the contaminated area can be covered by new data samples generated via combinations of data with correct labels. A feature learning-based objective is added to serve as an optimization constraint to regularize the network and further enhance the robustness w.r.t. anomaly contamination. Extensive experiments on 11 real-world datasets show that our approach significantly outperforms state-of-the-art competitors by 20%-30% in AUC-PR and obtains more robust and superior performance in settings with different anomaly contamination levels and varying numbers of labeled anomalies. The source code is available at https://***/xuhongzuo/rosas/. © 2023, CC BY.

关键词： Contamination

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MTD: Multi-Timestep Detector for Delayed Streaming Perception

arXiv

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arXiv 2023年

作者： Huang, Yihui Chen, Ningjiang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Key Laboratory of Parallel Distributed and Intelligent Computing Guangxi University Education Department of Guangxi Zhuang Autonomous Region Nanning China

Autonomous driving systems require real-time environmental perception to ensure user safety and experience. Streaming perception is a task of reporting the current state of the world, which is used to evaluate the delay and accuracy of autonomous driving systems. In real-world applications, factors such as hardware limitations and high temperatures inevitably cause delays in autonomous driving systems, resulting in the offset between the model output and the world state. In order to solve this problem, this paper propose the Multi-Timestep Detector (MTD), an end-to-end detector which uses dynamic routing for multi-branch future prediction, giving model the ability to resist delay fluctuations. A Delay Analysis Module (DAM) is proposed to optimize the existing delay sensing method, continuously monitoring the model inference stack and calculating the delay trend. Moreover, a novel Timestep Branch Module (TBM) is constructed, which includes static flow and adaptive flow to adaptively predict specific timesteps according to the delay trend. The proposed method has been evaluated on the Argoverse-HD dataset, and the experimental results show that it has achieved state-of-the-art performance across various delay settings. Copyright © 2023, The Authors. All rights reserved.

关键词： Autonomous vehicles

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Deep Isolation Forest for Anomaly Detection

arXiv

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arXiv 2022年

作者： Xu, Hongzuo Pang, Guansong Wang, Yijie Wang, Yongjun The College of Computer National University of Defense Technology Changsha410073 China The Science and Technology on Parallel and Distributed Processing Laboratory China The School of Computing and Information Systems Singapore Management University 178902 Singapore

Isolation forest (iForest) has been emerging as arguably the most popular anomaly detector in recent years due to its general effectiveness across different benchmarks and strong scalability. Nevertheless, its linear axis-parallel isolation method often leads to (i) failure in detecting hard anomalies that are difficult to isolate in high-dimensional/non-linear-separable data space, and (ii) notorious algorithmic bias that assigns unexpectedly lower anomaly scores to artefact regions. These issues contribute to high false negative errors. Several iForest extensions are introduced, but they essentially still employ shallow, linear data partition, restricting their power in isolating true anomalies. Therefore, this paper proposes deep isolation forest. We introduce a new representation scheme that utilises casually initialised neural networks to map original data into random representation ensembles, where random axis-parallel cuts are subsequently applied to perform the data partition. This representation scheme facilitates high freedom of the partition in the original data space (equivalent to non-linear partition on subspaces of varying sizes), encouraging a unique synergy between random representations and random partition-based isolation. Extensive experiments show that our model achieves significant improvement over state-of-the-art isolation-based methods and deep detectors on tabular, graph and time series datasets;our model also inherits desired scalability from iForest. © 2022, CC BY.

关键词： Anomaly detection

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A Deep Belief Networks Intrusion Detection Method Based on Generative Adversarial Networks

A Deep Belief Networks Intrusion Detection Method Based on G...

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Artificial Intelligence, Networking and Information Technology (AINIT), International Seminar on

作者： Yi Wei Yingxiong Nong Zhe Li Jian Pan Zhenyu Yang Zezhang Zheng Ningjiang Chen China Tobacco Guangxi Industrial Co. Ltd Nanning China School of Computer Electronics and Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Key Laboratory of Parallel Distributed and Intelligent Computing School of Computer Electronics and Information Guangxi University (Guangxi University) Nanning China

ISBN: (数字)9798350385557

ISBN: (纸本)9798350385564

In response to the substantial threat that Internet attacks pose to data center network security, researchers have proposed several deep learning-based methods for detecting network intrusions. However, while algorithms are constantly improving in terms of accuracy, their stability in the face of insufficient attack samples is a major obstacle. To solve the issues of insufficient attack samples and low detection accuracy in network intrusion detection, this paper proposes a deep confidence network intrusion detection method G-DBN based on GAN. The model is based on the malicious sample extension of the generative adversarial network, and it can produce adversarial samples using malicious network flows as original samples. Furthermore, this paper uses deep belief network technology to create and assess the efficacy of the G-DBN model in detecting network attacks, comparing it to standard DBN models and other network intrusion detection techniques. Experimental results show that compared to the standard three-layer DBN method, the G-DBN method described in this paper improves the detection accuracy of attack samples by 6.46% and better meets the performance requirements of current practical applications.

关键词： Seminars Learning systems Data centers Accuracy Network intrusion detection Network security Generative adversarial networks

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Cooperative Air-Ground Instant Delivery by UAVs and Crowdsourced Taxis

Cooperative Air-Ground Instant Delivery by UAVs and Crowdsou...

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International Conference on Data Engineering

作者： Junhui Gao Qianru Wang Xin Zhang Juan Shi Xiang Zhao Qingye Han Yan Pan School of Computer Science Northwestern Polytechnical University School of Computer Science and Technology Xidian University National Key Laboratory of Information Systems Engineering National University of Defense Technology Air Force Engineering University Laboratory for Big Data and Decision National University of Defense Technology School of Management Science and Real Estate Chongqing University National Key Laboratory of Parallel and Distributed Computing National University of Defense Technology

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

Instant delivery has become a fundamental service in people's daily lives. Different from the traditional express service, the instant delivery has a strict shipping time constraint after being ordered. However, the labor shortage makes it challenging to realize efficient instant delivery. To tackle the problem, researchers have studied to introduce vehicles (i.e., taxis) or Unmanned Aerial Vehicles (UAVs or drones) into instant delivery tasks. Unfortunately, the delivery detour of taxis and the limited battery of UAVs make it hard to meet the rapidly increasing instant delivery demands. Under this circumstance, this paper proposes an air-ground cooperative instant delivery paradigm to maximize the delivery performance and meanwhile minimize the negative effects on the taxi passengers. Specifically, a data-driven delivery potential-demands-aware cooperative strategy is designed to improve the overall delivery performance of both UAVs and taxis as well as the taxi passengers' experience. The experimental results show that the proposed method improves the delivery number by 30.1% and 114.5% compared to the taxi-based and UAV-based instant delivery respectively, and shortens the delivery time by 35.7% compared to the taxi-based instant delivery.

关键词： Autonomous aerial vehicles Data engineering Air to ground communication Trajectory Batteries Time factors Task analysis

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Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression

Knowledge Distillation with Source-free Unsupervised Domain ...

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International Conference on Computer Supported Cooperative Work in Design

作者： Jing Tian Juan Chen Ning Jiang Chen Lin Bai Suqun Huang School of Computer and Electronic Information Guangxi University Nanning China Guangxi Intelligent Digital Services Research Center of Engineering Technology Nanning China Education Department of Guangxi Zhuang Autonomous Region Key Laboratory of Parallel Distributed and Intelligent Computing (Guangxi University) Nanning China

The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.

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Overview of blockchain consensus mechanism 2020

Overview of blockchain consensus mechanism

引用

2nd International Conference on Big Data Engineering, BDE 2020

作者： Zhang, Changqiang Wu, Cangshuai Wang, Xinyi Key Laboratory of Parallel and Distributed Computing College of Computer National University of Defense Technology China College of Computer National University of Defense Technology China

ISBN: (纸本)9781450377225

The advent of the Bitcoin system has brought another boom in the Internet era. In a very short time, many Blockchain systems come into being successively, whose decentration, consensus mechanisms, intelligent contract, and other characteristics make them applicable to various fields such as finance, education, medical, technology, etc. The consensus mechanism is the core of Blockchain technology. And a good consensus mechanism plays a very important role in the stable operation of the Blockchain system. The continuous improvement of consensus mechanisms such as PoW (proof-of-work), PoS (proof-of-stake), DPoS (delegated-proof-of-stake), and PBFT (Practical Byzantine Fault Tolerance) has led to the evolution of Blockchain technology to Blockchain 3.0. Starting from the issue of Byzantine generals, this article analyzes common consensus mechanisms based on existing Blockchain applications and then evaluates their consistency. This paper has a unique perspective, selection and comment on the latest progress and important literature in the field of block chain. At last, it also has a high degree of summary and prospect to the existing problems and the future development trend. © 2020 ACM.

关键词： Blockchain

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LTKT: Knowledge Tracing Based on Positive and Negative Learning Transfers

SSRN

引用

SSRN 2023年

作者： Xu, Jia Tang, Rongrong Lv, Pin Yu, Minghe Yu, Ge Chen, Enhong School of Computer Electronics and Information Guangxi University GuangXi Nanning530004 China Key Laboratory of Parallel Distributed and Intelligent Computing Education Department of Guangxi Zhuang Autonomous Region Guangxi530004 China Northeastern University Shenyang110819 China University of Science and Technology of China Hefei230088 China

Knowledge Tracing (KT) is a critical but challenging problem for many educational applications. As an essential part of educational psychology, the propagated influence among pedagogical concepts (i.e., learning transfer) is important for optimizing KT tasks. However, existing KT methods only consider the positive learning transfer and neglect the negative learning transfer. To this end, in this paper, we propose a novel deep knowledge tracing model, called positive and negative Learning Transfers based Knowledge Tracing (LTKT). To the best of our knowledge, LTKT makes the first attempt to concurrently utilize the positive and negative learning transfer relations among concepts to improve KT results. First, LTKT employs a statistical-based approach to construct a learning transfer graph (LTG). Then, LTKT quantifies the impact of an exercise's practice result on the knowledge state of the concept via a direct learning effect component, after which a learning transfer effect component is carefully designed to quantify the impact of the practice result on the knowledge states of neighboring concepts based on the positive and negative learning transfer relations modeled by LTG. We conduct extensive experiments on public real-world datasets, and the experimental results show that LTKT outperforms all state-of-the-art KT methods and has good interpretability. © 2023, The Authors. All rights reserved.

关键词： Deep neural networks

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