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arXiv 2025年

作者： Zhu, Yan Zhu, Jingyang Wang, Ting Shi, Yuanming Jiang, Chunxiao Letaief, Khaled Ben MoE Engineering Research-Center of Software/Hardware Co-design Technology and Application The Shanghai Key Lab. of Trustworthy Computing East China Normal University Shanghai200062 China School of Information Science and Technology ShanghaiTech University Shanghai201210 China Beijing National Research Center for Information Science and Technology Tsinghua University Beijing100084 China Department of Electronic and Computer Engineering The Hong Kong University of Science and Technology Hong Kong

Advancements in artificial intelligence (AI) and low-earth orbit (LEO) satellites have promoted the application of large remote sensing foundation models for various downstream tasks. However, direct downloading of these models for fine-tuning on the ground is impeded by privacy concerns and limited bandwidth. Satellite federated learning (FL) offers a solution by enabling model fine-tuning directly on-board satellites and aggregating model updates without data downloading. Nevertheless, for large foundation models, the computational capacity of satellites is insufficient to support effective on-board fine-tuning in traditional satellite FL frameworks. To address these challenges, we propose a satellite-ground collab.rative federated fine-tuning framework. The key of the framework lies in how to reasonably decompose and allocate model components to alleviate insufficient on-board computation capabilities. During fine-tuning, satellites exchange intermediate results with ground stations or other satellites for forward propagation and back propagation, which brings communication challenges due to the special communication topology of space transmission networks, such as intermittent satellite-ground communication, short duration of satellite-ground communication windows, and unstable inter-orbit inter-satellite links (ISLs). To reduce transmission delays, we further introduce tailored communication strategies that integrate both communication and computing resources. Specifically, we propose a parallel intra-orbit communication strategy, a topology-aware satellite-ground communication strategy, and a latency-minimalization inter-orbit communication strategy to reduce space communication costs. Simulation results demonstrate significant reductions in training time with improvements of approximately 33%. Copyright © 2025, The Authors. All rights reserved.

关键词： Satellite communication systems

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On the Convergence Rate of a Class of Proximal-Based Decomposition Methods for Monotone Variational Inequalities

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Journal of the Operations Research Society of China 2015年第3期3卷 347-362页

作者： Xiang-Feng Wang Shanghai Key Lab for Trustworthy Computing Software Engineering InstituteEast China Normal UniversityShanghai 200062China

A unified efficient algorithm framework of proximal-based decomposition methods has been proposed for monotone variational inequalities in 2012,while only global convergence is proved at the same *** this paper,we give a unified proof on theO(1/t)iteration complexity,together with the linear convergence rate for this kind of proximal-based decomposition *** theε-optimal iteration complexity result defined by variational inequality,the non-ergodic relative error of adjacent iteration points is also proved to decrease in the same ***,the linear convergence rate of this algorithm framework can be constructed based on some special variational inequality properties,without necessary strong monotone conditions.

关键词： Variational inequality Proximal point algorithm Iteration complexity Relative error Convergence rate Error bound

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A novel spatio-temporal trajectory data-driven development approach for autonomous vehicles

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Frontiers of Earth Science 2021年第3期15卷 620-630页

作者： Menghan ZHANG Mingjun MA Jingying ZHANG Mingzhuo ZHANG Bo LIW Dehui DU School of Software and Engineering East China Normal UniversityShanghai 200062China Shanghai Key Laboratory of Trustworthy Computing East China Normal UniversityShanghai 200062China MOE International Joint Lab of Trustworthy Software East China Normal UniversityShanghai 200062China

Nowadays,autonomous driving has been attracted widespread attention from academia and *** we all know,deep learning is effective and essential for the development of AI components of Autonomous Vehicles(AVs).However,it is challenging to adopt multi-source heterogenous data in deep ***,we propose a novel data-driven approach for the delivery of high-quality Spatio-Temporal Trajectory Data(STTD)to AVs,which can be deployed to assist the development of AI components with deep *** novelty of our work is that the meta-model of STTD is constructed based on the domain knowledge of autonomous *** approach,including collection,preprocessing,storage and modeling of STTD as well as the training of AI components,helps to process and utilize huge amount of STTD *** further demonstrate the usability of our approach,a case study of vehicle behavior prediction using Long Short-Term Memory(LSTM)networks is *** results show that our approach facilitates the training process of AI components with the STTD.

关键词： spatio-temporal trajectory data data metamodeling domain knowledge LSTM vehicle behavior prediction AI component

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Modeling Multilevel Business Process Monitoring via BPMN Extension

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Concurrency and Computation: Practice and Experience 2025年第12-14期37卷

作者： Kang, Guosheng Wang, Zhen Cheng, Hangyu Liu, Jianxun Wen, Yiping Peng, Jun School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Hunan Provincial Key Lab. for Services Computing and Novel Software Technology Hunan University of Science and Technology Xiangtan China School of Computer Science and Technology Xi'an Jiaotong University Xi'an China School of Computer Science Fudan University Shanghai China

Business process monitoring involves real-time supervision of a series of activities carried out by an organization to achieve specific objectives. Process event monitoring points (PEMP) are used to pinpoint specific locations within a process model where expected events are anticipated to occur. However, in general business process modeling languages, such as business process model and notation (BPMN), there is a lack of explicit modeling for PEMPs. This article proposes a method for modeling pairwise event monitoring points in business process models to track the execution status of specific activities or process segments via BPMN extension. Specifically, process monitoring points are designed by expanding the modeling element of sequence flow. Moreover, the business process model is decomposed using the refined process structure tree (RPST) to verify the soundness of the designed pairwise monitoring points. The proposed method allows flexible monitoring at the process segment level. And the process monitoring data could be used for a clear understanding of business progress, especially useful in heterogeneous business process model to support decision-making. Through case study from real-world business processes, the effectiveness and usefulness of the proposed process monitoring modeling method is validated. © 2025 John Wiley & Sons Ltd.

关键词： Electronic trading

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More Than Address: Pre-Identify Your Income with the Open Data

More Than Address: Pre-Identify Your Income with the Open Da...

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International Conference on Cloud computing and Big Data, CCBD 2015

作者： Li, Ruoyu Xiong, Huanyu Zhao, Hui Software Engineering Institute East China Normal University Shanghai China Shanghai Key Lab. of Trustworthy Computing East China Normal University China

ISBN: (纸本)9781467383509

Modern e-commerce websites collect immense amounts of transaction data, and almost each of them is associated with at least one delivery address. To promote sales, e-commerce operators are eager to identify the customers consumption levels. However, the information resource for target customers is quite limited due to privacy protection. In this paper, we propose an integrated framework together with an empirical method to pre-identify customers purchasing ability. To implement that, we need to recognize the implicit information within the delivery addresses of customers. In practice, both transaction data from the e-commerce providers and open real estate data associated with the delivery addresses are made rational utilization of. The experimental results show that this method is a feasible solution for income segment prediction, which can be extensively applied to marketing, advertising and product promotion, etc. © 2015 IEEE.

关键词： Sales

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A New Energy Efficient VM Scheduling Algorithm for Cloud computing Based on Dynamic Programming 4

A New Energy Efficient VM Scheduling Algorithm for Cloud Com...

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4th IEEE International Conference on Cyber Security and Cloud computing, CSCloud 2017 and 3rd IEEE International Conference of Scalab.e and Smart Cloud, SSC 2017

作者： Zhang, Kepi Wu, Tong Chen, Siyuan Cai, Linsen Peng, Chao Shanghai Key Lab of Trustworthy Computing School of Computer Science and Software Engineering East China Normal University Shanghai China

ISBN: (纸本)9781509066438

As a new computing paradigm, cloud computing has significantly contributed to the rapid development of massive data centers. However, the corresponding energy issue becomes increasingly challenging. In this paper, we focus on the energy saving issue for virtual machine (VM) selections on an overloaded host in a cloud computing environment. We analyze the energy influencing factors during a VM migration, then design energy efficient VM selection algorithms based on greedy algorithm and dynamic programming method. We conduct experiments with CloudSim and results show that the proposed algorithm in this paper can effectively reduce energy consumption while satisfying the SLA constraints. © 2017 IEEE.

关键词： Dynamic programming

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STeC: A location-triggered specification language for real-time systems

STeC: A location-triggered specification language for real-t...

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作者： Chen, Yixiang MoE Engineering Research Center for Software/Hardware Co-design Technology and Application Shanghai Key Lab. for Trustworthy Computing East China Normal University Shanghai 200062 China

ISBN: (纸本)9780769546698

Internet of Things (IoT) or Cyber-Physical Systems (CPS) is a new trend of real-time systems in the area of information technology. This paper introduces a spatiotemporal consistence language for real-time systems (Shortly, STeC). The consistence requires that a process do its tasks at the required location or time. Thus, this language provides a location-triggered specification for real-time systems. The interaction between real-time agents deals with agent2agent communications. STeC looks like an extension of precess algebra CSP. But, the execution-time of actions and status of agents are stressed. Two kinds of interrupts time and interaction break are considered. Following the Dijkstra's guard style, nondeterministic choice phase guarded by communications is introduced. After setting up the syntax, its operational semantics is introduced. As an example, the railroad crossing problem is specified in terms of this language STeC. © 2012 IEEE.

关键词： Specification languages

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Sparsing and Smoothing for the seq2seq Models

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2023年第3期4卷 464-472页

作者： Zhao, Shuai Liang, Zhuoqian Wen, Jinming Chen, Jie College of Information Science and Technology Jinan University Guangzhou511400 China Pazhou Lab Guangzhou510330 China Shanghai Key Laboratory of Trustworthy Computing School of Software Engineering East China Normal University Shanghai200062 China

Current neural language models are trained to minimize cross-entropy and use softmax to compute the locally normalized probabilities over the target. While this setup provides solid results in several natural language processing (NLP) tasks, one unsatisfying aspect is its dense output. This density is wasteful, making models hard to interpret and assigning probability mass to many implausible outputs. To overcome this problem, we propose T-softmax, a simple but effective method to draw considerably sparse probability out of neural language models than softmax. Our method avoids dense output by truncating the unreliable tail of the probability distribution to improve the model's performance. In addition, we generalize logits with temperature, a critical regularization technique, from the softmax to T-softmax. To show our approach as a drop-in replacement for softmax, we evaluate them on three NLP tasks: summary generation, question answer, and math word problem. Experimental results show that our proposed model significantly improves performance without sacrificing speed;notably, in all experiments, our method outperforms the softmax. © 2020 IEEE.

关键词： Probability distributions

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Boundary-Aware Periodicity-based Sparsification Strategy for Ultra-Long Time Series Forecasting 24

Boundary-Aware Periodicity-based Sparsification Strategy for...

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32nd ACM International Conference on Multimedia, MM 2024

作者： Bao, Yiying Zhou, Hao Peng, Chao Xu, Chenyang Shi, Shuo Cai, Kecheng East China Normal University Shanghai China Software Engineering Institute Shanghai Key Lab of Trustworthy Computing East China Normal University Shanghai China

ISBN: (纸本)9798400706868

In various domains such as transportation, resource management, and weather forecasting, there is an urgent need for methods that can provide predictions over a sufficiently long time horizon to encompass the period required for decision-making and implementation. Compared to traditional time series forecasting, ultra-long time series forecasting requires enhancing the model's ability to infer long time series, while maintaining inference costs within an acceptable range. To address this challenge, we propose the Boundary-Aware Periodicity-based sparsification strategy for Ultra-Long time series forecasting (BAP-UL).This method effectively captures periodic features in time series and reorganizes inputs and outputs into shorter sub-sequences for improved prediction accuracy. In the paper, we investigate several commonly used benchmark datasets and demonstrate that the proposed method can yield comparable performance across them. © 2024 ACM.

关键词： Weather forecasting

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Green Federated Learning over Cloud-RAN with Limited Fronthaul and Quantized Neural Networks 3

Green Federated Learning over Cloud-RAN with Limited Frontha...

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Wang, Jiali Mao, Yijie Wang, Ting Shi, Yuanming Shanghai Key Lab. of Trustworthy Computing East China Normal University MoE Engineering Research Center of Software/Hardware Co-Design Technology and Application China China

ISBN: (纸本)9798350333732

In this paper, we investigate a green federated learning (FL) framework over cloud radio access network (Cloud-RAN) system that comprises a server, multiple devices and remote radio heads (RRHs). Each device utilizes quantized neural networks (QNNs) and sends quantized model parameters to the server to save energy consumption via RRHs. The server aggregates all the signals to update the global model parameters and broadcasts the updated parameters to the selected devices. In this context, we propose an energy consumption model for the QNN training and communication model over Cloud-RAN. We develop an energy minimization problem based on the proposed energy model. We jointly design fronthaul rate allocation, device transmit power, and precision level of QNNs while ensuring target accuracy, transmit power budget and limited fronthaul capacity. Guided by the convergence analysis, we adopt alternative optimization method to solve the energy minimization problem. The simulation outcomes demonstrate that the FL framework suggested can considerably diminish energy usage in comparison to other traditional methods. This has immense potential in realizing a sustainable and eco-friendly FL over Cloud-RAN. © 2023 IEEE.

关键词： Radio access networks

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