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International Conference on computer Supported Cooperative Work in Design

作者： Jiayan Xiang Wanjun Chen Yang Wang Bowen Liang Zihao Liu Guosheng Kang 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 HNUST Xiangtan China

With the development of Mashup technique, the number of Web APIs released on the Web continues to grow year by year. However, it is a challenging issue to find and select the desirable Web APIs among the large amount of Web APIs. Consequently, interactive Web API recommendation is used to alleviate the difficulty of service selection, when users or developers try to invoke Web APIs for solving their business requirements or software development requirements. Currently, there are several collaborative filtering based approaches proposed for Web API recommendation, while their recommendation performance is limited on both optimality and scalability. This paper proposes a light neural graph collaborative filtering based Web API recommendation approach, named LNGCF. Specifically, LNGCF learns user and item embeddings by linearly propagating them on the user-item interaction graph, and uses the weighted summation of the embeddings learned at all layers as the final embedding. Such simple, linear, and neat model is much easier to implement and train. A set of experiments are conducted on a real-world dataset. Experimental results demonstrate the substantial improvements on both optimality and scalability over the baselines.

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Time-Aware Missing Traffic Flow Prediction for Sensors with Privacy-Preservation 11th

Time-Aware Missing Traffic Flow Prediction for Sensors with ...

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11th International Conference on computer Engineering and Networks, CENet2021

作者： Qi, Lianyong Wang, Fan Xu, Xiaolong Dou, Wanchun Zhang, Xuyun Khosravi, Mohammad R. Zhou, Xiaokang School of Computer Science Qufu Normal University Qufu China School of Computer and Software Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology Nanjing University of Information Science and Technology Nanjing China State Key Laboratory for Novel Software Technology Department of Computer Science and Technology Nanjing University Nanjing China Department of Computing Macquarie University SydneyNSW2109 Australia Department of Computer Engineering Persian Gulf University Bushehr Iran Department of Electrical and Electronic Engineering Shiraz University of Technology Shiraz Iran Faculty of Data Science Shiga University Hikone Japan

ISBN: (纸本)9789811665530

With the continuous development of IoT, a number of sensors establish on the roadside to monitor traffic conditions in real time. The continuously traffic data generated by these sensors makes traffic management feasible. However, loss of data may occur due to inevitable sensor failure, impeding traffic managers to understand traffic dynamics clearly. In this situation, it is becoming a necessity to predict missing traffic flow accurately for effective traffic management. Furthermore, the traffic sensor data are often distributed and stored by different agencies, which inhibits the multi-party sensor data sharing significantly due to privacy concerns. Therefore, it has become a major obstacle to balance the tradeoff between data sharing and vehicle privacy. In light of these challenges, we propose a privacy-aware and accurate missing traffic flow prediction approach based on time-aware Locality-Sensitive Hashing technique. At last, we deploy a set of experiments based on a real traffic dataset. Experimental reports demonstrate the feasibility of our proposal in terms of traffic flow prediction accuracy and efficiency while guaranteeing sensor data privacy. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Data privacy

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PG-LBO: Enhancing High-Dimensional Bayesian Optimization with Pseudo-label and Gaussian Process Guidance

arXiv

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arXiv 2023年

作者： Chen, Taicai Duan, Yue Li, Dong Qi, Lei Shi, Yinghuan Gao, Yang National Key Laboratory for Novel Software Technology Nanjing University China Huawei Noah’s Ark Lab China School of Computer Science and Engineering Southeast University China

Variational Autoencoder based Bayesian Optimization (VAE-BO) has demonstrated its excellent performance in addressing high-dimensional structured optimization problems. However, current mainstream methods overlook the potential of utilizing a pool of unlabeled data to construct the latent space, while only concentrating on designing sophisticated models to leverage the labeled data. Despite their effective usage of labeled data, these methods often require extra network structures, additional procedure, resulting in computational inefficiency. To address this issue, we propose a novel method to effectively utilize unlabeled data with the guidance of labeled data. Specifically, we tailor the pseudo-labeling technique from semi-supervised learning to explicitly reveal the relative magnitudes of optimization objective values hidden within the unlabeled data. Based on this technique, we assign appropriate training weights to unlabeled data to enhance the construction of a discriminative latent space. Furthermore, we treat the VAE encoder and the Gaussian Process (GP) in Bayesian optimization as a unified deep kernel learning process, allowing the direct utilization of labeled data, which we term as Gaussian Process guidance. This directly and effectively integrates the goal of improving GP accuracy into the VAE training, thereby guiding the construction of the latent space. The extensive experiments demonstrate that our proposed method outperforms existing VAE-BO algorithms in various optimization scenarios. Our code will be published at https://***/TaicaiChen/PG-LBO. Copyright © 2023, The Authors. All rights reserved.

关键词： Gaussian distribution

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FM-FCN:A Neural Network with Filtering Modules for Accurate Vital Signs Extraction

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Research 2025年第1期2024卷 92-106页

作者： Fangfang Zhu Qichao Niu Xiang Li Qi Zhao Honghong Su Jianwei Shuai Department of Physics and Fujian Provincial Key Laboratory for Soft Functional Materials ResearchXiamen UniversityXiamen 361005China National Institute for Data Science in Health and Medicine and State Key Laboratory of Cellular Stress BiologyInnovation Center for Cell Signaling NetworkXiamen UniversityXiamen 361005China Vitalsilicon Technology Co.Ltd. JiaxingZhejiang 314006China School of Computer Science and Software Engineering University of Science and Technology LiaoningAnshan 114051China Yangtze Delta Region Institute of Tsinghua University ZhejiangJiaxing 314006China Wenzhou Institute University of Chinese Academy of SciencesWenzhou 325001China Oujiang Laboratory (Zhejiang Lab for Regenerative Medicine Vision and Brain Health)Wenzhou 325001China

Neural networks excel at capturing local spatial patterns through convolutional modules,but they may struggle to identify and effectively utilize the morphological and amplitude periodic nature of physiological *** this work,we propose a novel network named filtering module fully convolutional network(FM-FCN),which fuses traditional filtering techniques with neural networks to amplify physiological signals and suppress ***,instead of using a fully connected layer,we use an FCN to preserve the time-dimensional correlation information of physiological signals,enabling multiple cycles of signals in the network and providing a basis for signal ***,we introduce the FM as a network module that adapts to eliminate unwanted interference,leveraging the structure of the *** approach builds a bridge between deep learning and signal processing ***,we evaluate the performance of FM-FCN using remote *** results demonstrate that FM-FCN outperforms the second-ranked method in terms of both blood volume pulse(BVP)signal and heart rate(HR)*** substantially improves the quality of BVP waveform reconstruction,with a decrease of 20.23%in mean absolute error(MAE)and an increase of 79.95%in signal-to-noise ratio(SNR).Regarding HR estimation accuracy,FM-FCN achieves a decrease of 35.85%in MAE,29.65%in error standard deviation,and 32.88%decrease in 95%limits of agreement width,meeting clinical standards for HR accuracy *** results highlight its potential in improving the accuracy and reliability of vital sign measurement through high-quality BVP signal *** codes and datasets are available online at https://***/zhaoqi106/FM-FCN.

关键词： physiological signalsin filtering module fully convolutional network fm fcn which vital signs extraction amplify physiological signals convolutional modulesbut neural networks filtering module capturing local spatial patterns

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Energy-Guided Diffusion Sampling for Offline-to-Online Reinforcement Learning

arXiv

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arXiv 2024年

作者： Liu, Xu-Hui Liu, Tian-Shuo Jiang, Shengyi Chen, Ruifeng Zhang, Zhilong Chen, Xinwei Yu, Yang National Key Laboratory for Novel Software Technology Nanjing University China School of Artificial Intelligence Nanjing University China Polixir Technologies China Department of Computer Science The University of Hong Kong Hong Kong

Combining offline and online reinforcement learning (RL) techniques is indeed crucial for achieving efficient and safe learning where data acquisition is expensive. Existing methods replay offline data directly in the online phase, resulting in a significant challenge of data distribution shift and subsequently causing inefficiency in online fine-tuning. To address this issue, we introduce an innovative approach, Energy-guided DIffusion Sampling (EDIS), which utilizes a diffusion model to extract prior knowledge from the offline dataset and employs energy functions to distill this knowledge for enhanced data generation in the online phase. The theoretical analysis demonstrates that EDIS exhibits reduced suboptimality compared to solely utilizing online data or directly reusing offline data. EDIS is a plug-in approach and can be combined with existing methods in offline-to-online RL setting. By implementing EDIS to off-the-shelf methods Cal-QL and IQL, we observe a notable 20% average improvement in empirical performance on MuJoCo, AntMaze, and Adroit environments. Code is available at https://***/liuxhym/EDIS. © 2024, CC BY.

关键词： Adversarial machine learning

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Joint Mobile Edge Caching and Pricing: A Mean-Field Game Approach

Joint Mobile Edge Caching and Pricing: A Mean-Field Game App...

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International Conference on Data Engineering

作者： Yin Xu Xichong Zhang Mingjun Xiao Jie Wu An Liu Sheng Zhang School of Computer Science and Technology University of Science and Technology of China Hefei China Suzhou Institute for Advanced Research University of Science and Technology of China Suzhou China Department of Computer and Information Sciences Temple University Philadelphia USA School of Computer Science and Technology Soochow University Suzhou China State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

ISBN: (数字)9798350317152

ISBN: (纸本)9798350317169

In this paper, we investigate the competitive content placement problem in Mobile Edge Caching (MEC) systems, where Edge Data Providers (EDPs) cache appropriate contents and trade them with requesters at a suitable price. Most of the existing works ignore the complicated strategic and economic interplay between content caching, pricing, and content sharing. Therefore, we propose a joint Mean-Field Game framework for mobile edge Caching and Pricing (MFG-CP) in large-scale dynamic MEC systems, which can facilitate distributed optimal decision-making based on the mean-field game theory. Specifi-cally, we first formulate the competitive content placement issue among EDPs as a non-cooperative stochastic differential game. To significantly reduce the communication and computation complexity, we further devise a mean-field model to approximate the collective impact of all EDPs on caching, trading, and sharing, by which each EDP can quickly estimate some unknown information without considerable interactions. Then, we develop a distributed best response scheme based on iterative learning, enabling each EDP to solely customize its optimal caching strategy and pricing policy. Besides, we theoretically prove the existence of a unique MFG equilibrium. Finally, trace-driven simulations demonstrate the effectiveness of MFG-CP compared with some baselines.

关键词： Economics Computational modeling Stochastic processes Pricing Games Differential games Data engineering

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State/Action-based Fairness Verification for Non-determinism

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Chinese Journal of Electronics 2023年第4期20卷 603-606页

作者： XU Guangquan PANG Shanchen GAO Honghao XIAO Yingyuan LI Xiaohong FENG Zhiyong School of Computer Science and Technology Tianjin University Tianjin China Department of Computer Science and Technology Tsinghua University Beijing China College of Information Science and Engineering Shandong University of Science and Technology Qingdao China School of Computer Engineering and Science Shanghai University Shanghai China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology Tianjin University of Technology Tianjin China

Most of the existing work about fairness verification was carried out by checking the fairness based on state, action, or process respectively. It is hard to resolve the fairness issue for complex software systems, especially in life-critical systems and safety-critical systems. In this paper, we verify the fairness based on both state and action, which is realized mainly by introducing an extended Kripke structure and four kinds of fairness (weak global/local fairness and strong global/local fairness). The experimental results show the superiority of our method by the savings in memory (2.08%) and time consumption (26.45%).

关键词： Memory management software systems

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ESA: Example Sieve Approach for Multi-Positive and Unlabeled Learning

arXiv

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arXiv 2024年

作者： Li, Zhongnian Wei, Meng Ying, Peng Xu, Xinzheng School of Computer Science and Technology China University of Mining and Technology Xuzhou China Mine Digitization Engineering Research Center of the Ministry of Education Xuzhou China State Key Lab. for Novel Software Technology Nanjing University Nanjing China

Learning from Multi-Positive and Unlabeled (MPU) data has gradually attracted significant attention from practical applications. Unfortunately, the risk of MPU also suffer from the shift of minimum risk, particularly when the models are very flexible as shown in Fig.1. In this paper, to alleviate the shifting of minimum risk problem, we propose an Example Sieve Approach (ESA) to select examples for training a multi-class classifier. Specifically, we sieve out some examples by utilizing the Certain Loss (CL) value of each example in the training stage and analyze the consistency of the proposed risk estimator. Besides, we show that the estimation error of proposed ESA obtains the optimal parametric convergence rate. Extensive experiments on various real-world datasets show the proposed approach outperforms previous methods. Source code is available at https://***/WilsonMqz/ESA Copyright © 2024, The Authors. All rights reserved.

关键词： Contrastive Learning

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Coordination of networking and computing: toward new information infrastructure and new services mode

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Frontiers of Information technology & Electronic Engineering 2024年第5期25卷 629-632页

作者： Xiaoyun WANG Tao SUN Yong CUI Rajkumar BUYYA Deke GUO Qun HUANG Hassnaa MOUSTAFA Chen TIAN Shangguang WANG China Mobile Communications Corporation Beijing 100032China China Mobile Research Institute Beijing 100053China Department of Computer Science and Technology Tsinghua UniversityBeijing 100084China CLOUDS Lab School of Computing and Information SystemsUniversity of MelbourneMelbourneVIC 3010Australia National Key Laboratory of Information Systems Engineering National University of Defense TechnologyChangsha 410073China School of Computer Science Peking UniversityBeijing 100871China Intel Corporation Santa ClaraCA 95054USA State Key Laboratory for Novel Sofiware Technology Nanjing UniversityNanjing 210023China State Key Laboratory of Networking and Switching Technology Bejing University of Posts and TelecommunicationsBeijing 100876China

Computing,serving as the cornerstone of information processing,plays a pivotal role in the digital service ***"network"and"computing,"responsible for information transmission and processing respectively,traditionally belong to different stakeholders and have evolved ***,the recent trend toward the coordination and integration of computing and networks has garnered significant attention from both industry and *** such as"computility network"and"computing force network"have emerged,and the International Telecommunication Union-Telecommunication Standardization(ITU-T)has initiated efforts to develop standards for the coordination of networking and computing(CNC),focusing on the architecture and framework.

关键词： computing services network

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CopGAT: Co-propagation Self-supervised Graph Attention Network

CopGAT: Co-propagation Self-supervised Graph Attention Netwo...

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IEEE International Conference on Big Data and Cloud Computing (BdCloud)

作者： Baoming Zhang Ming Xu Mingcai Chen Mingyuan Chen Chongjun Wang Department of Computer Science and Technology State Key Laboratory for Novel Software Technology Nanjing University Nanjing China

Graph Attention Network (GAT) is one of the state-of-the-art architectures for Graph Neural Networks (GNNs). In this paper, we first propose label Purity to explore the relationship between the graph attention and the node labels. By tracking the label purity of graph attention, we observe that graph attention suppresses message passing between inter-class nodes in the graph with homophily. Upon such basis, we further improve graph attention's capability to capture label information by designing a self-supervised graph attention loss. We propose a novel approach based on self-supervised graph attention loss, Co-propagation Self-supervised Graph Attention Network (CopGAT), which explicitly restrains message passing between inter-class nodes. The Co-propagation means propagating node representations in Euclidean space and pseudo labels in probability simplex via the shared attention matrix, combining Message Passing Neural Networks and label Propagation Algorithm under the graph attention framework. Constrained with the self-supervised graph attention loss generated by co-propagation, our CopGAT enhances the ability of GAT to attenuate the influence of structural noise. Furthermore, our self-supervised graph attention loss can be extended to other graph attention models in a plug-and-play manner. Experimental results demonstrate that our Cop GAT outperforms previous state-of-the-art methods on five benchmark datasets for the semi-supervised node classification task.

关键词： Message passing Benchmark testing Graph neural networks Task analysis

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