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30th IEEE International Conference on Parallel and Distributed systems, ICPADS 2024

作者： Xiong, Tianchen Peng, Bo Shanghai Jiao Tong University School of Software Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (纸本)9798331515966

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to 17 × latency improvement over AWS Lambda and up to 42 × latency improvement over OpenFaaS when the data size is 200M. It also saves up to 6 0% CPU overhead at runtime compared to OpenFaaS. © 2024 IEEE.

关键词： Data Integrity Computation Lossless Hardware Offloading Serverless systems Storage Computation

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Distributed Model Interpretation for Vertical Federated Learning with Feature Discrepancy 31

Distributed Model Interpretation for Vertical Federated Lear...

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31st IEEE/ACM International Symposium on Quality of Service, IWQoS 2023

作者： Xing, Rui Zheng, Zhenzhe Li, Qinya Wu, Fan Chen, Guihai Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems China

ISBN: (纸本)9798350399738

Vertical federated learning (VFL) allows multiple clients with misaligned feature spaces to collaboratively accomplish the global model training. Applying VFL to high stakes decision scenarios greatly requires model interpretation for decision reliability and diagnosis. However, the feature discrepancy in VFL raises new issues for model interpretation in distributed setting: one is from the local-global perspective, where the local importance of features is not equal to the global importance;and the other is from the local-local perspective, where information asymmetry among clients causes difficulty in identifying overlapped features. In this work, we propose a new distributed Model Interpretation method for Vertical Federated Learning with feature discrepancy, namely MI-VFL. In particular, to deal with the local-global discrepancy, MI-VFL leverages the law of total probability to adjust the local importance of features and ensures the completeness of the selected features using adversarial game. To handle the local-local discrepancy, MI-VFL builds a federated adversarial learning model to efficiently identify the overlapped features once, rather than performing client-to-client intersections multiple times. We extensively evaluate MI-VFL on six synthetic datasets and five real-world datasets. The evaluation results reveal that MI-VFL can accurately identify the important features, suppress the overlapped features, and thus improve the model performance. © 2023 IEEE.

关键词： Learning systems

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Attentive Hawkes Process Application for Sequential Recommendation 28th

Attentive Hawkes Process Application for Sequential Recomme...

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28th International Conference on Database systems for Advanced Applications, DASFAA 2023

作者： Yu, Shuodian Ma, Li Gao, Xiaofeng Guo, Jianxiong Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China Advanced Institute of Natural Sciences Beijing Normal University Zhuhai519087 China

ISBN: (纸本)9783031306716

Sequential recommendation systems which aim to predict the users’ future clicking items are essential in both research and industry services. Based on the contextual characteristics of users, a large number of existing methods exploit abundant information from user behavior sequences and gain users’ interests through expressive sequential models like recurrent neural networks and self-attention mechanism to predict the next item. However, the temporal generative process of behavior sequences, which is crucial to the complex relationships and distributions of users’ dynamic preferences, is rarely considered. In this paper, we propose HawRec (Hawkes process based sequential recommendation), which is a new representation learning approach to model the interacted sequences of users from a temporal point process perspective. In particular, we leverage temporal self-attention mechanism to make an analogy to the self-exciting mechanism of Hawkes process. We bring expressive superiority of neural network into the Hawkes process with non-parametric conditional intensity kernel, resulting in more accurate recommendation and inheriting robustness from the temporal point process. Furthermore, extensive experiments conducted on four public benchmarks demonstrate that our HawRec outperforms various baselines of state-of-the-art sequential recommendation. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Recurrent neural networks

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DICFaaS: Parallel and scalable Data Integrity Computation for Serverless systems

DICFaaS: Parallel and Scalable Data Integrity Computation fo...

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International Conference on Parallel and Distributed systems (ICPADS)

作者： Tianchen Xiong Bo Peng School of Software Shanghai Jiao Tong University Shanghai China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai China

ISBN: (数字)9798331515966

ISBN: (纸本)9798331515973

Serverless computing is extensively employed in distributed storage computing scenarios owing to its dynamic responsiveness and on-demand allocation capabilities. Within distributed storage computing, data integrity computation holds significant importance. Nevertheless, current serverless systems fail to consider the CPU-intensive nature of data integrity computation, leading to prolonged CPU occupancy, increased latency, and substantial performance deterioration, particularly in parallel scenarios. Therefore, there is an imperative need for a solution that can efficiently handle this kind of computation in serverless systems. In this paper, we introduce DICFaaS, a high-performance serverless system specialized for parallel and scalable data integrity computation. DICFaaS can recognize the offloading potential of servers and automatically offload CPU-intensive data integrity computation. The offloading function of DICFaaS can be easily integrated into other existing serverless systems. We evaluate the effectiveness of DICFaaS for data integrity computation compared to state-of-the-art serverless systems. For example, DICFaaS delivers up to $17 \times$ latency improvement over AWS Lambda and up to $42 \times$ latency improvement over OpenFaaS when the data size is 200M. It also saves up to $\mathbf{6 0 \%}$ CPU overhead at runtime compared to OpenFaaS.

关键词： Runtime Data integrity Heuristic algorithms Scalability Serverless computing Distributed databases Quality of service Parallel processing Dynamic scheduling Hardware

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PathGPT: Leveraging Large Language Models for Personalized Route Generation

arXiv

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arXiv 2025年

作者： Marcelyn, Steeve Cuthbert Gao, Yucen Zhang, Yuzhe Gao, Xiaofeng Chen, Guihai Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China

The proliferation of GPS-enabled devices has led to the accumulation of a substantial corpus of historical trajectory data. By leveraging these data for training machine learning models, researchers have devised novel data-driven methodologies that address the personalized route recommendation (PRR) problem. In contrast to conventional algorithms such as Dijkstra’s shortest path algorithm, these novel algorithms possess the capacity to discern and learn patterns within the data, thereby facilitating the generation of more personalized paths. However, once these models have been trained, their application is constrained to the generation of routes that align with their training patterns. This limitation renders them less adaptable to novel scenarios and the deployment of multiple machine learning models might be necessary to address new possible scenarios, which can be costly as each model must be trained separately. Inspired by recent advances in the field of Large Language Models (LLMs), we leveraged their natural language understanding capabilities to develop a unified model to solve the PRR problem while being seamlessly adaptable to new scenarios without additional training. To accomplish this, we combined the extensive knowledge LLMs acquired during training with further access to external hand-crafted context information, similar to RAG (Retrieved Augmented Generation) systems, to enhance their ability to generate paths according to user-defined requirements. Extensive experiments on different datasets show a considerable uplift in LLM performance on the PRR problem. © 2025, CC BY.

关键词： Natural language processing systems

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Distributed Model Interpretation for Vertical Federated Learning with Feature Discrepancy

Distributed Model Interpretation for Vertical Federated Lear...

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International Workshop on Quality of Service

作者： Rui Xing Zhenzhe Zheng Qinya Li Fan Wu Guihai Chen Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China

Vertical federated learning (VFL) allows multiple clients with misaligned feature spaces to collaboratively accomplish the global model training. Applying VFL to high stakes decision scenarios greatly requires model interpretation for decision reliability and diagnosis. However, the feature discrepancy in VFL raises new issues for model interpretation in distributed setting: one is from the local-global perspective, where the local importance of features is not equal to the global importance; and the other is from the local-local perspective, where information asymmetry among clients causes difficulty in identifying overlapped features. In this work, we propose a new distributed Model Interpretation method for Vertical Federated Learning with feature discrepancy, namely MI-VFL. In particular, to deal with the local-global discrepancy, MI-VFL leverages the law of total probability to adjust the local importance of features and ensures the completeness of the selected features using adversarial game. To handle the local-local discrepancy, MI-VFL builds a federated adversarial learning model to efficiently identify the overlapped features once, rather than performing client-to-client intersections multiple times. We extensively evaluate MI-VFL on six synthetic datasets and five real-world datasets. The evaluation results reveal that MI-VFL can accurately identify the important features, suppress the overlapped features, and thus improve the model performance.

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Hybrid deep learning model for risk prediction of fracture in patients with diabetes and osteoporosis

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Frontiers of Medicine 2022年第3期16卷 496-506页

作者： Yaxin Chen Tianyi Yang Xiaofeng Gao Ajing Xu Department of Pharmacy Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China Shanghai Key Laboratory of Scalable Computing and Systems Department of Computer Science and EngineeringShanghai Jiao Tong UniversityShanghai 200240China Clinical Research Unit Xinhua Hospital Affiliated to Shanghai Jiao Tong University School of MedicineShanghai 200240China

The fracture risk of patients with diabetes is higher than those of patients without diabetes due to hyperglycemia,usage of diabetes drugs,changes in insulin levels,and excretion,and this risk begins as early as *** factors including demographic data(such as age,height,weight,and gender),medical history(such as smoking,drinking,and menopause),and examination(such as bone mineral density,blood routine,and urine routine)may be related to bone metabolism in patients with ***,most of the existing methods are qualitative assessments and do not consider the interactions of the physiological factors of *** addition,the fracture risk of patients with diabetes and osteoporosis has not been further studied *** this paper,a hybrid model combining XGBoost with deep neural network is used to predict the fracture risk of patients with diabetes and osteoporosis,and investigate the effect of patients’physiological factors on fracture risk.A total of 147 raw input features are considered in our *** presented model is compared with several benchmarks based on various metrics to prove its ***,the top 18 influencing factors of fracture risks of patients with diabetes are determined.

关键词： XGBoost deep neural network healthcare risk prediction

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A Lightweight Encoder-Decoder Framework for Carpooling Route Planning

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IEEE Transactions on Mobile computing 2025年

作者： Gao, Yucen Ma, Li Yu, Zhemeng Zhang, Songjian Gao, Hui Fang, Jun Gao, Xiaofeng Shanghai Jiao Tong University Shanghai Key Laboratory of Scalable Computing and Systems College of Computer Science and Engineering Shanghai200240 China Didi Global Inc. Beijing100190 China

Carpooling Route Planning (CRP) has become an important issue with the growth of low-carbon traffic systems. We investigate a novel, meaningful and challenging scenario for CRP in industry, called Multi-Candidate Carpooling Route Planning (MCRP) problem, where each passenger may have several potential positions to get on and off the car. We surprisingly notice that this problem can be easily generalized for similar services such as express, takeout, or crowdsensing services, which means MCRP is a new fundamental combinatorial optimization problem. Traditional graph search algorithms or indexing methods are usually time and space consuming or perform poorly, which are not suitable for solving the problem. In this paper, we propose an end-to-end encoder-decoder model to plan a route for each many-to-one carpooling order with various data-driven mechanisms such as graph partitioning and feature crossover. The encoder is a filter-integrated Graph Convolution Network with external information fusion combining a supervised pre-training classification task, while the latter mimics a pointer network with a rule-based mask mechanism and a domain feature crossover module. We validate the effectiveness and efficiency of our model based on both synthetic and real-world datasets. Our code is available at https://***/GaoYucen/Lite-GD © 2002-2012 IEEE.

关键词： Network coding

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MONETA: Prior-free and Truthful Auctions with Adaptive Reserve Price for High Revenue in Dynamic Spectrum Access Networks

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Chinese Journal of Electronics 2023年第2期21卷 355-361页

作者： Xiaobing Wu Guihai Chen Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiaotong University Shanghai China

Auction is efficient for spectrum allocation in future dynamic spectrum access networks. Truthful or strategy-proof auction is favorable since every bidder only needs to bid his true valuation and the auctioneer assigns spectra to bidders who value them most. Existing truthful spectrum auction schemes however either generate very low revenue for the auctioneer or need extra prior distribution information on the true valuation of bidders. Low revenue generation could discourage the auctioneers from leasing their spectra and it is usually hard to get prior information on bidders' true evaluations on the spectra. In this paper, we propose a class of truthful spectrum auction schemes which bring higher revenue for the spectrum owners and do not need any prior information at the same time. We present both theoretical and simulation results of our proposed auction schemes.

关键词： Upper bound Adaptive systems Simulation Dynamic spectrum access Probability Resource management Cost accounting

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Quality over Quantity: Boosting Data Efficiency Through Ensembled Multimodal Data Curation

arXiv

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arXiv 2025年

作者： Xu, Jinda Song, Yuhao Wang, Daming Zhao, Weiwei Chen, Minghua Chen, Kangliang Li, Qinya Shanghai Key Laboratory of Scalable Computing and Systems Shanghai Jiao Tong University China HAOMO.AI Technology Co. Ltd. China

In an era overwhelmed by vast amounts of data, the effective curation of web-crawl datasets is essential for optimizing model performance. This paper tackles the challenges associated with the unstructured and heterogeneous nature of such datasets. Traditional heuristic curation methods often inadequately capture complex features, resulting in biases and the exclusion of relevant data. We introduce an advanced, learning-driven approach, Ensemble Curation Of DAta ThroUgh Multimodal Operators (EcoDatum), incorporating a novel quality-guided deduplication method to ensure balanced feature distributions. EcoDatum strategically integrates various unimodal and multimodal data curation operators within a weak supervision ensemble framework, utilizing automated optimization to score each data point effectively. EcoDatum, which significantly improves the data curation quality and efficiency, outperforms existing state-of-the-art (SOTA) techniques, ranked 1st on the DataComp leaderboard, with an average performance score of 0.182 across 38 diverse evaluation datasets. This represents a 28% improvement over the DataComp baseline method, demonstrating its effectiveness in improving dataset curation and model training efficiency. © 2025, CC BY-NC-SA.

关键词： Data quality

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