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CUTE: A collaborative Fusion Representation-Based Fine-Tuning and Retrieval Framework for Code Search 19th

CUTE: A Collaborative Fusion Representation-Based Fine-Tunin...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Song, Qihong Liu, Jianxun Hu, Haize School of Computer Science and Engineering Hunan University of Science and Technology Xiangtan China Key Laboratory of Knowledge Processing and Networked Manufacturing Hunan University of Science and Technology Hunan Xiangtan China

ISBN: (纸本)9783031545207

Code search aims at searching semantically related code snippets from the large-scale database based on a given natural descriptive query. Fine-tuning pre-trained models for code search tasks has recently emerged as a new trend. However, most studies fine-tune models merely using metric learning, overlooking the beneficial effect of the collaborative relationship between code and query. In this paper, we introduce an effective fine-tuning and retrieval framework called CUTE. In the fine-tuning component, we propose a collaborative Fusion Representation (CFR) consisting of three stages: pre-representation, collaborative representation, and residual fusion. CFR enhances the representation of code and query, considering token-level collaborative features between code and query. Furthermore, we apply augmentation techniques to generate vector-level hard negative samples for training, which further improves the ability of the pre-trained model to distinguish and represent features during fine-tuning. In the retrieval component, we introduce a two-stage retrieval architecture that includes pre-retrieval and refined ranking, significantly reducing time and computational resource consumption. We evaluate CUTE with three advanced pre-trained models on CodeSearchNet consisting of six programming languages. Extensive experiments demonstrate the fine-tuning effectiveness and retrieval efficiency of CUTE. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Query processing

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Multi-agent Reinforcement Learning Based collaborative Multi-task Scheduling for Vehicular Edge computing 19th

Multi-agent Reinforcement Learning Based Collaborative Multi...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Li, Peisong Xiao, Ziren Wang, Xinheng Huang, Kaizhu Huang, Yi Tchernykh, Andrei School of Advanced Technology Xi’an Jiaotong-Liverpool University Suzhou215123 China Data Science Research Center and Division of Natural and Applied Sciences Duke Kunshan University Suzhou215316 China Department of Electrical Engineering and Electronics University of Liverpool LiverpoolL69 3BX United Kingdom CICESE Research Center carr. Tijuana-Ensenada 3918 BC Ensenada22860 Mexico

ISBN: (纸本)9783031545306

Nowadays, connected vehicles equipped with advanced computing and communication capabilities are increasingly viewed as mobile computing platforms capable of offering various in-vehicle services, including but not limited to autonomous driving, collision avoidance, and parking assistance. However, providing these time-sensitive services requires the fusion of multi-task processing results from multiple sensors in connected vehicles, which poses a significant challenge to designing an effective task scheduling strategy that can minimize service requests’ completion time and reduce vehicles’ energy consumption. In this paper, a multi-agent reinforcement learning-based collaborative multi-task scheduling method is proposed to achieve a joint optimization on completion time and energy consumption. Firstly, the reinforcement learning-based scheduling method can allocate multiple tasks dynamically according to the dynamic-changing environment. then, a cloud-edge-end collaboration scheme is designed to complete the tasks efficiently. Furthermore, the transmission power can be adjusted based on the position and mobility of vehicles to reduce energy consumption. the experimental results demonstrate that the designed task scheduling method outperforms benchmark methods in terms of comprehensive performance. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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FedCL: An Efficient Federated Unsupervised Learning for Model Sharing in IoT 18th

FedCL: An Efficient Federated Unsupervised Learning for Mode...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Zhao, Chen Gao, Zhipeng Wang, Qian Mo, Zijia Yu, Xinlei Beijing Univ Posts & Telecommun State Key Lab Networking & Switching Technol Beijing Peoples R China Beijing Univ Technol Beijing Peoples R China

ISBN: (纸本)9783031243820;9783031243837

Federated Learning (FL) continues to make significant advances, solving model sharing under privacy-preserving. However, these existing methods are only of limited utility in the Internet of things (IoT) scenarios, as they either heavily depend on high-quality labeled data or only perform well under idealized conditions, which typically cannot be found in practical applications. As such, a natural problem is how to leverage unlabeled data among multiple clients to optimize sharing model. To address this shortcoming, we propose Federated Contrastive Learning (FedCL), an efficient federated learning method for unsupervised image classification. the proposed FedCL can be summarized in three steps: distributed federated pretraining of the local model using contrastive learning, supervised fine-tuning on a server with few labeled data, and distillation with unlabeled examples on each client for refining and transferring the personalized-specific knowledge. Extensive experiments show that our method outperforms all baseline methods by large margins, including 69.32% top-1 accuracy on CIFAR-10, 85.75% on SVHN, and 74.64% on Mini-ImageNet with the only use of 1% labels.

关键词： Federated learning Internet of things Self-supervised learning Unsupervised learning

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A Context-Aware Approach to Scheduling of Multi-Data-Source Tasks in Mobile Edge computing 18th

A Context-Aware Approach to Scheduling of Multi-Data-Source ...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Chen, Jifeng Yang, Yang Southwest Univ Coll Comp & Informat Sci Coll Software Chongqing Peoples R China

ISBN: (纸本)9783031243820;9783031243837

the multi-data-source tasks are prevalent in the Mobile Edge computing environment due to the distributed data storage of continuous data streams sent by various mobile devices. Many existing studies on context-aware task scheduling in MEC mainly aim to reduce energy consumption and improve performance by computation offloading among the MEC nodes. However, task context implies not only the locations of data sources but also the receivers of the result of task execution. therefore, in this paper, we build a context-aware model for the multi-data-source tasks to analyze the objectives of task execution and data management in MEC and propose a related approach to scheduling the multi-data-source tasks. In the proposed approach, the task and environment contexts are used to determine the data sources involved, generate the task execution strategy, and resolve the target receivers. We discuss the task scheduling scheme in detail, including its architecture, metadata and data management, context-aware scheduling algorithm, and task offloading. We evaluate the feasibility and effectiveness of the proposed approach on a dataset of taxi trajectories in a city. the results illustrate that the proposed approach can effectively schedule the context-aware multi-data-source tasks in a MEC environment.

关键词： Multi-data-source task Context-aware scheduling Mobile edge computing

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Cloud-Edge-Device collaborative Image Retrieval and Recognition for Mobile Web 19th

Cloud-Edge-Device Collaborative Image Retrieval and Recogni...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Huang, Yakun Li, Wenwei Wu, Shouyi Qiao, Xiuquan Guo, Meng He, Hongshun Li, Yang Beijing University of Posts and Telecommunications Beijing100876 China China Mobile Communications Research Institute Beijing100053 China

ISBN: (纸本)9783031545276

Efficient image retrieval and recognition are pivotal for optimal mobile web vision services. Traditional web-based solutions offer limited accuracy, high overhead, and struggle with vast image volumes. Transferring images for real-time cloud recognition demands stable communication, and large-scale concurrent requests strain computational and network resources. this paper introduces a distributed recognition approach, leveraging cloud-edge-device collaboration through edge computing’s low latency and high bandwidth. We present a lightweight image saliency detection model tailored for mobile web, enhancing initial image feature extraction. Additionally, we introduce an edge-based, deep learning-driven method to amplify image retrieval speed and precision. We incorporate a location and popularity-based caching system to alleviate strains on cloud resources and network bandwidth during extensive image requests. Our real-world tests validate our approach: our saliency detection model outpaces the benchmark by reducing the model size by up to 94%, making it suitable for mobile web deployment. the proposed method improves retrieval accuracy by 40% over cloud-based counterparts and cuts response latency by over 60%. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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Roadside IRS Assisted Task Offloading in Vehicular Edge computing Network 19th

Roadside IRS Assisted Task Offloading in Vehicular Edge Com...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Xie, Yibin Shi, Lei Li, Zhehao Ding, Xu Liu, Feng School of Computer Science and Information Engineering Hefei University of Technology Hefei230009 China Engineering Research Center of Safety Critical Industrial Measurement and Control Technology Ministry of Education Hefei230009 China

ISBN: (纸本)9783031545207

Vehicular edge computing (VEC) has been recognized as a promising technique to process delay-sensitive vehicular applications. Nevertheless, in order to accommodate the rapid growth in the number of connected vehicles, it’s inevitable that there will be an increasing deployment of conventional infrastructure with limited communication ranges. this could potentially lead to escalating costs and impede the full realization of the VEC system. In this paper, a roadside intelligent reflecting surface (IRS) assisted VEC network is introduced, where the IRS is deployed outside the coverage of roadside units (RSUs) to extend the service range. Furthermore, the maximum total number of successful offloading tasks problem within the scheduling time problem is formulated, encompassing the optimization of offloading decisions, computation resource allocation and phase shift of IRS. To tackle the formulated challenging problem, we first decouple the original problem into two subproblems. then, a heuristic algorithm is proposed, where a many-to-one matching algorithm is proposed to joint optimize offloading decision and the computation resource, and an iterative algorithm is utilized to optimize the phase shift coefficients of IRS. the simulation results validate the effectiveness of the proposed algorithm in comparison to other schemes, and the IRS can effectively maintain network performance even when there are intervals in RSU coverage areas. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Resource allocation

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DQN-Based applications Offloading with Multiple Interdependent Tasks in Mobile Edge computing 19th

DQN-Based Applications Offloading with Multiple Interdepend...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Tu, Jiaxue Zhu, Dongge Xia, Yunni Li, Yin Ma, Yong Li, Fan Peng, Qinglan College of Computer Science Chongqing University Chongqing China Electric Power Research Institute of State Grid Ningxia Electric Power Co. Ltd. Ningxia Yinchuan China Guangzhou Institute of Software Application Technology Guangzhou China School of Computer and Information Engineering Jiangxi Normal University Jiangxi Nanchang China Sichuan University Chengdu China School of Artificial Intelligence Henan University Zhengzhou China

ISBN: (纸本)9783031545207

Recently, Vehicular Edge computing (VEC) is evolving as a solution for offloading computationally intensive tasks in in-vehicle environments. However, when the number of vehicles and users is large, pure edge resources may be insufficient and limited, most existing work focuses on minimizing system latency by designing some offloading strategies. therefore, hybrid multilayer edge structures are in dire require of mission deployment strategies that can synthesize cost and mission latency. In this paper, we argue that each application can be decomposed into multiple interdependent subtasks, and that the different subtasks can be deployed separately into different edge layers in a hybrid three-tier edge computing infrastructure for execution. We develop an improved DQN task deployment algorithm based on Lyapunov optimization to jointly optimize the average workflow latency and cost under a long-term cost constraint, and simulation results clearly show that, comparing with the traditional approach, our proposed method effectively reduces the cost consumption by 92.8% while sacrificing only some latency. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Computation offloading

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Edge Server Deployment Approach Based on Uniformity and Centrality 19th

Edge Server Deployment Approach Based on Uniformity and Ce...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Jiang, Xinghong Ma, Yong Xia, Yunni Xie, Qilin Jian, Wenxin School of Computer and Information Engineering Jiangxi Normal University Nanchang330027 China School of Computer Science Chongqing University Chongqing China School of Digital Industry Jiangxi Normal University Shangrao334000 China

ISBN: (纸本)9783031545207

In mobile Internet applications that support edge computing, the deployment scheme of edge servers affects the business operation state. Traditional edge servers are deployed on base stations, which do not fully extend the service range of edge servers, resulting in difficult access to edge services. therefore, this paper proposes the Edge Server Deployment Approach Based on Uniformity and Centrality (ESDA-UC). ESDA-UC considers intersections as candidate deployment locations for edge servers, taking into account traffic density and road network structure. Connection centrality, between centrality, base station centrality, and traffic density are used as the main factors. the intersection centrality of each intersection is calculated as the selection criteria for the deployment location. To avoid concentrating the coverage of edge servers in developed regions of the city, we allocate the number of edge servers according to regional importance. Finally, the improved greedy algorithm is utilized to generate a deployment plan for edge servers. Experiments show that ESDA-UC has higher base station coverage, vehicle coverage, and vehicle coverage time ratios compared to the baseline method. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Edge computing

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An Energy-Saving Strategy for 5G Base Stations in Vehicular Edge computing 18th

An Energy-Saving Strategy for 5G Base Stations in Vehicular ...

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18th European-Alliance-for-Innovation (EAI) international conference on collaborative computing - networking, applications and worksharing (CollaborateCom)

作者： Zhao, Fei Shi, Lei Shi, Yi Zhao, Shuangliang Lv, Zengwei Hefei Univ Technol Sch Comp Sci & Informat Engn Hefei 230009 Peoples R China Virginia Tech Dept ECE Blacksburg VA 24061 USA Minist Educ Engn Res Ctr Safety Crit Ind Measurement & Contro Hefei 230009 Peoples R China

ISBN: (纸本)9783031243820;9783031243837

With the rapid development of the Internet of Vehicles (IoV), various types of compute-intensive vehicle applications are emerging and present significant challenges to resource-constrained vehicles. Emerging vehicular edge computing (VEC) can alleviate this situation by offloading computational tasks from vehicles to base stations (BSs) with edge servers at the roadside. And the excellent transmission performance of 5G provides more reliable support for VEC. However, due to the drawbacks of small coverage area and high energy cost of 5G BSs, long-term usage will result in huge costly resource investment. In this paper, we design a new 4G-5G hybrid task offloading framework for the VEC scenario. We consider switching some of the 5G BSs to sleep state during low traffic and low data consumption conditions, while letting the 4G BS process the tasks generated in these areas. We first build the mathematical model and find that it cannot be solved directly. then we design the algorithm for the offline case and the online case, respectively. Simulation results show that our scheme significantly reduces the energy cost while ensuring high task success rate.

关键词： 5G Energy saving Vehicular edge computing Task offloading Internet of vehicles

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Probabilistic Inference Based Incremental Graph Index for Similarity Search on Social Networks 19th

Probabilistic Inference Based Incremental Graph Index for S...

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19th EAI international conference on collaborative computing: networking, applications and worksharing, CollaborateCom 2023

作者： Lu, Tong Qi, Zhiwei Yue, Kun Duan, Liang School of Information Science and Engineering Yunnan University Kunming China Yunnan Key Laboratory of Intelligent Systems and Computing Yunnan University Kunming China

ISBN: (纸本)9783031545276

To find k neighbor users on social networks, the efficient approximate nearest neighbor search (ANNS) is useful. Existing graph index methods have shown attractive performance, but suffer from inaccuracy w.r.t. unindexed queries. To achieve both indexed and unindexed queries for graph-index methods, we propose an incremental graph index based method for ANNS on social networks. First, graph convolutional network based on attention mechanism is adopted to embed the social network into low-dimensional vector space, on which the graph index is constructed efficiently. To add the unindexed queries to the graph index incrementally, we propose Bayesian network (BN) learned from social interactions to represent dependency relations of unindexed queries and their neighbors, and perform probabilistic inferences in BN to infer the closest neighbors of unindexed queries. Extensive experiments show that our proposed method outperforms the state-of-the-art methods on both execution time and precision. © ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering 2024.

关键词： Vector spaces

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