Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152684
Aimei Dong, Zhiyun Qi, Yi Zhai, Guohua Lv
Epilepsy is a neurological disease that occurs in all ages and seriously threatens physical and mental health. There are two problems in the present study. One is the limitation of the amount of publicly available medical data. And the other is that the distributions of the data are different but correlated. Conventional machine learning methods are not applicable. But transfer learning method has shown promising performance in solving both problems. In this paper, a multi-source domain transfer learning method called MDTL for epilepsy diagnosis is proposed. In order to fully exploit the specific features and common features of the dataset, we propose a domain specific feature extractor and a common feature extractor. For enhancing data, we transform the signals into time-frequency diagrams to rotate and crop. The three types of electrocardiogram (ECG) time-frequency diagram are put to train model, and the model is transferred to electroencephalogram (EEG) time-frequency diagrams. The results confirm that MDTL is effective in epilepsy diagnosis.
{"title":"Multi-Source Domain Transfer Learning on Epilepsy Diagnosis","authors":"Aimei Dong, Zhiyun Qi, Yi Zhai, Guohua Lv","doi":"10.1109/CSCWD57460.2023.10152684","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152684","url":null,"abstract":"Epilepsy is a neurological disease that occurs in all ages and seriously threatens physical and mental health. There are two problems in the present study. One is the limitation of the amount of publicly available medical data. And the other is that the distributions of the data are different but correlated. Conventional machine learning methods are not applicable. But transfer learning method has shown promising performance in solving both problems. In this paper, a multi-source domain transfer learning method called MDTL for epilepsy diagnosis is proposed. In order to fully exploit the specific features and common features of the dataset, we propose a domain specific feature extractor and a common feature extractor. For enhancing data, we transform the signals into time-frequency diagrams to rotate and crop. The three types of electrocardiogram (ECG) time-frequency diagram are put to train model, and the model is transferred to electroencephalogram (EEG) time-frequency diagrams. The results confirm that MDTL is effective in epilepsy diagnosis.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"83-88"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74834007","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152725
Beibei Cheng, Yiming Zhu, Yuxuan Chen, Xiaodan Gu, Kai Dong
The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.
{"title":"Privacy Protection Based on Packet Filtering for Home Internet-of-Things","authors":"Beibei Cheng, Yiming Zhu, Yuxuan Chen, Xiaodan Gu, Kai Dong","doi":"10.1109/CSCWD57460.2023.10152725","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152725","url":null,"abstract":"The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"16 1","pages":"1214-1219"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75553588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Deep Reinforcement Learning (DRL) combines the perceptual capabilities of deep learning with the decision-making capabilities of Reinforcement Learning RL, which can achieve enhanced decision-making. However, the environmental state data contains the privacy of the users. There exists consequently a potential risk of environmental state information being leaked during RL training. Some data desensitization and anonymization technologies are currently being used to protect data privacy. There may still be a risk of privacy disclosure with these desensitization techniques. Meanwhile, policymakers need the environmental state to make decisions, which will cause the disclosure of raw environmental data. To address the privacy issues in DRL, we propose a differential privacy-based online DRL algorithm. The algorithm will add Gaussian noise to the gradients of the deep network according to the privacy budget. More important, we prove tighter bounds for the privacy budget. Furthermore, we train an autocoder to protect the raw environmental state data. In this work, we prove the privacy budget formulation for differential privacy-based online deep RL. Experiments show that the proposed algorithm can improve privacy protection while still having relatively excellent decisionmaking performance.
{"title":"A Privacy-Preserving Online Deep Learning Algorithm Based on Differential Privacy","authors":"Jun Li, Fengshi Zhang, Yonghe Guo, Siyuan Li, Guanjun Wu, Dahui Li, Hongsong Zhu","doi":"10.1109/CSCWD57460.2023.10152847","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152847","url":null,"abstract":"Deep Reinforcement Learning (DRL) combines the perceptual capabilities of deep learning with the decision-making capabilities of Reinforcement Learning RL, which can achieve enhanced decision-making. However, the environmental state data contains the privacy of the users. There exists consequently a potential risk of environmental state information being leaked during RL training. Some data desensitization and anonymization technologies are currently being used to protect data privacy. There may still be a risk of privacy disclosure with these desensitization techniques. Meanwhile, policymakers need the environmental state to make decisions, which will cause the disclosure of raw environmental data. To address the privacy issues in DRL, we propose a differential privacy-based online DRL algorithm. The algorithm will add Gaussian noise to the gradients of the deep network according to the privacy budget. More important, we prove tighter bounds for the privacy budget. Furthermore, we train an autocoder to protect the raw environmental state data. In this work, we prove the privacy budget formulation for differential privacy-based online deep RL. Experiments show that the proposed algorithm can improve privacy protection while still having relatively excellent decisionmaking performance.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"559-564"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81634318","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152777
Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang
In past work, deep learning researchers always designed hyperparameters such as model structure and learning rate first and then used the training set to train the weights in this model. While unrestricted model structure design leads to massive neuron redundancy in neural network models. By pruning these redundant neurons, not only can the storage be compressed effectively, but also the operation can be accelerated. In this paper, we propose a method to utilize the training set to prune the model structure during training: 1) train the initialized model and bring it to basic convergence; 2) feed the entire training set into the model and calculate the activations of neurons in each layer; 3) calculate the threshold for neuron pruning in each layer according to the pruning ratio, delete neurons whose activation value is lower than the threshold, and correspondingly delete the weights of the upper and lower layers; 4) further train the pruned model so that it eventually converges. This method of deleting redundant neurons not only greatly deletes the parameters in the model but also achieves model acceleration. We applied this method to some mainstream neural network models: VGGNet and ResNet, and achieved good results.
{"title":"Neural Network Model Pruning without Additional Computation and Structure Requirements","authors":"Yin Xie, Yigui Luo, Haihong She, Zhaohong Xiang","doi":"10.1109/CSCWD57460.2023.10152777","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152777","url":null,"abstract":"In past work, deep learning researchers always designed hyperparameters such as model structure and learning rate first and then used the training set to train the weights in this model. While unrestricted model structure design leads to massive neuron redundancy in neural network models. By pruning these redundant neurons, not only can the storage be compressed effectively, but also the operation can be accelerated. In this paper, we propose a method to utilize the training set to prune the model structure during training: 1) train the initialized model and bring it to basic convergence; 2) feed the entire training set into the model and calculate the activations of neurons in each layer; 3) calculate the threshold for neuron pruning in each layer according to the pruning ratio, delete neurons whose activation value is lower than the threshold, and correspondingly delete the weights of the upper and lower layers; 4) further train the pruned model so that it eventually converges. This method of deleting redundant neurons not only greatly deletes the parameters in the model but also achieves model acceleration. We applied this method to some mainstream neural network models: VGGNet and ResNet, and achieved good results.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"17 1","pages":"1734-1740"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89679863","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152789
Xiang Cao, Ningjiang Chen, Xuemei Yuan, Yifei Liu
With the support of 5G technology, mobile edge computing has made the application of industrial IoT and power IoT more and more extensive. By deploying a certain number of edge servers at the edge of the network, network service delay may significantly reduce. For the IoT scenario where the content demand is unpredictable, there are multiple distributed cloud servers and the distributed cloud servers do not communicate directly, a feasible way to improve the network service quality is to dynamically optimize the storage of edge servers and formulate targeted caching strategies. This paper proposes an edge caching approach based on multi-agent deep deterministic policy gradient named MADDPG-C, which regards distributed cloud servers and edge servers as different types of agents and maximizes the efficiency of edge caching in cooperation and competition. Simulation experiments show that the proposed MADDPG-C can further improve the hit rate of the edge cache and reduce the waiting delay of terminal devices.
{"title":"A Cooperative Edge Caching Approach Based on Multi-Agent Deep Reinforcement Learning","authors":"Xiang Cao, Ningjiang Chen, Xuemei Yuan, Yifei Liu","doi":"10.1109/CSCWD57460.2023.10152789","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152789","url":null,"abstract":"With the support of 5G technology, mobile edge computing has made the application of industrial IoT and power IoT more and more extensive. By deploying a certain number of edge servers at the edge of the network, network service delay may significantly reduce. For the IoT scenario where the content demand is unpredictable, there are multiple distributed cloud servers and the distributed cloud servers do not communicate directly, a feasible way to improve the network service quality is to dynamically optimize the storage of edge servers and formulate targeted caching strategies. This paper proposes an edge caching approach based on multi-agent deep deterministic policy gradient named MADDPG-C, which regards distributed cloud servers and edge servers as different types of agents and maximizes the efficiency of edge caching in cooperation and competition. Simulation experiments show that the proposed MADDPG-C can further improve the hit rate of the edge cache and reduce the waiting delay of terminal devices.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"41 7","pages":"1772-1777"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72471002","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152746
Fengyuan Shi, Zhou-yu Zhou, Wei Yang, Shu Li, Qingyun Liu, Xiuguo Bao
In a static network, attackers can easily launch network attacks on target hosts which have long-term constant IP addresses. In order to defend against attackers effectively, many defense approaches use IP hopping to dynamically transform IP configuration. However, these approaches usually focus on one type of network attacks, scanning attacks or Denial of Service (DoS) attacks, and cannot sense network situations. This paper proposes AHIP, an adaptive IP hopping method for moving target defense (MTD) to defend against different network attacks. We use a trained lightweight one-dimensional convolutional neural network (1D-CNN) detector to judge whether there are no attacks, scanning attacks or DoS attacks in the network, which can adaptively trigger corresponding IP hopping strategy. We use specific hardware and software to create the software defined network (SDN) environment for experiments. The experiments prove that AHIP performs better to thwart network attacks and has lower system overhead.
{"title":"AHIP: An Adaptive IP Hopping Method for Moving Target Defense to Thwart Network Attacks","authors":"Fengyuan Shi, Zhou-yu Zhou, Wei Yang, Shu Li, Qingyun Liu, Xiuguo Bao","doi":"10.1109/CSCWD57460.2023.10152746","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152746","url":null,"abstract":"In a static network, attackers can easily launch network attacks on target hosts which have long-term constant IP addresses. In order to defend against attackers effectively, many defense approaches use IP hopping to dynamically transform IP configuration. However, these approaches usually focus on one type of network attacks, scanning attacks or Denial of Service (DoS) attacks, and cannot sense network situations. This paper proposes AHIP, an adaptive IP hopping method for moving target defense (MTD) to defend against different network attacks. We use a trained lightweight one-dimensional convolutional neural network (1D-CNN) detector to judge whether there are no attacks, scanning attacks or DoS attacks in the network, which can adaptively trigger corresponding IP hopping strategy. We use specific hardware and software to create the software defined network (SDN) environment for experiments. The experiments prove that AHIP performs better to thwart network attacks and has lower system overhead.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"53 1","pages":"1300-1305"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76483630","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152708
Yi Gao, Fangfang Yuan, Cong Cao, Majing Su, Dakui Wang, Yanbing Liu
The Domain Name System (DNS), one of the essential basic services on the Internet, is often abused by attackers to launch various cyber attacks, such as phishing and spamming. Researchers have proposed many machine learning-based and deep learning-based methods to detect malicious domains. However, these methods rely on a large-scale dataset with labeled samples for model training. The fact is that the labeled domain samples are limited in the real-world DNS dataset. In this paper, we propose a few-shot malicious domain detection model named MetaDom, which employs a meta-learning algorithm for model optimization. Specifically, We first model the DNS scenario as a heterogeneous graph to capture richer information by analysing the complex relations among domains, IP addresses and clients. Then, we learn the domain representations with a heterogeneous graph neural network on the DNS HG. Finally, considering that only few labeled data are available in the real-world DNS scenario, a meta-learning algorithm with knowledge distillation is introduced to optimize the model. Extensive experiments on the real DNS dataset show that MetaDom outperforms other state-of-the-art methods.
{"title":"Few-shot Malicious Domain Detection on Heterogeneous Graph with Meta-learning","authors":"Yi Gao, Fangfang Yuan, Cong Cao, Majing Su, Dakui Wang, Yanbing Liu","doi":"10.1109/CSCWD57460.2023.10152708","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152708","url":null,"abstract":"The Domain Name System (DNS), one of the essential basic services on the Internet, is often abused by attackers to launch various cyber attacks, such as phishing and spamming. Researchers have proposed many machine learning-based and deep learning-based methods to detect malicious domains. However, these methods rely on a large-scale dataset with labeled samples for model training. The fact is that the labeled domain samples are limited in the real-world DNS dataset. In this paper, we propose a few-shot malicious domain detection model named MetaDom, which employs a meta-learning algorithm for model optimization. Specifically, We first model the DNS scenario as a heterogeneous graph to capture richer information by analysing the complex relations among domains, IP addresses and clients. Then, we learn the domain representations with a heterogeneous graph neural network on the DNS HG. Finally, considering that only few labeled data are available in the real-world DNS scenario, a meta-learning algorithm with knowledge distillation is introduced to optimize the model. Extensive experiments on the real DNS dataset show that MetaDom outperforms other state-of-the-art methods.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"62 1","pages":"727-732"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76791528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152792
Fuqing Zhao, Yuebao Liu, Tianpeng Xu
Selecting appropriate algorithms for specific problems has become a significant challenge with the remarkable growth of heuristics and meta-heuristics. To address this challenge, an iterative greedy selection hyper-heuristic algorithm with linear population size reduction (LIGSHH) was proposed in this paper. Using an iterative greedy strategy to choose the high level of exploration, this heuristic selects the Low-Level Heuristics (LLHs) that best suit the current problem. Nine LLHs are specifically designed for continuous optimization problems. Additionally, the exploration and exploitation capabilities of the LIGSHH are balanced by reducing the population size linearly at different stages of the problem. The proposed LIGSHH algorithm and comparison algorithms are tested on the CEC2017 benchmark test suite, and the experimental results show that the LIGSHH algorithm outperforms other comparison algorithms.
{"title":"Iterative Greedy Selection Hyper-heuristic with Linear Population Size Reduction","authors":"Fuqing Zhao, Yuebao Liu, Tianpeng Xu","doi":"10.1109/CSCWD57460.2023.10152792","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152792","url":null,"abstract":"Selecting appropriate algorithms for specific problems has become a significant challenge with the remarkable growth of heuristics and meta-heuristics. To address this challenge, an iterative greedy selection hyper-heuristic algorithm with linear population size reduction (LIGSHH) was proposed in this paper. Using an iterative greedy strategy to choose the high level of exploration, this heuristic selects the Low-Level Heuristics (LLHs) that best suit the current problem. Nine LLHs are specifically designed for continuous optimization problems. Additionally, the exploration and exploitation capabilities of the LIGSHH are balanced by reducing the population size linearly at different stages of the problem. The proposed LIGSHH algorithm and comparison algorithms are tested on the CEC2017 benchmark test suite, and the experimental results show that the LIGSHH algorithm outperforms other comparison algorithms.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"151 1","pages":"751-755"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79549566","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/cscwd57460.2023.10152801
{"title":"Keynote 2 : Promoting the diversity of digital technologies","authors":"","doi":"10.1109/cscwd57460.2023.10152801","DOIUrl":"https://doi.org/10.1109/cscwd57460.2023.10152801","url":null,"abstract":"","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"174 1","pages":""},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79664019","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152680
Xiaobo Wei, Peng Li, Weiyi Huang, Zhiyuan Liu, Qin Liu
Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework.
{"title":"Two-stage Vehicle Pair Dispatch in Multi-hop Ridesharing","authors":"Xiaobo Wei, Peng Li, Weiyi Huang, Zhiyuan Liu, Qin Liu","doi":"10.1109/CSCWD57460.2023.10152680","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152680","url":null,"abstract":"Ridesharing benefits the economy and the environment. In multi-hop ridesharing, passengers are permitted to switch vehicles within a single trip, extending the flexibility of conventional ridesharing. Nonetheless, vehicle dispatch is a difficult issue in multi-hop ridesharing. We subdivide the vehicle dispatching problem into the vehicle pairing problem and the request selection problem within a vehicle pair. To address these subproblems, we propose a two-stage framework for vehicle pair dispatching. In the initial stage, we model the vehicle pairing problem as a maximum vehicle-vehicle matching problem in a general graph, which differs from the conventional vehicle-request matching problem in a bipartite graph. The vehicle pairing algorithm is proposed to efficiently solve the vehicle pairing problem. In the second stage, we model the request selection problem as a multidimensional knapsack problem (d-KP) and propose an LP-relaxation request selection algorithm with an approximation ratio 1/5. Experiments conducted on a real-world dataset demonstrate the economic benefit of our proposed two-stage framework.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"67 1","pages":"255-260"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80437028","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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