Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00340
Yingying Yao, Xiaolin Chang, Lin Li, Jiqiang Liu, J. Misic, V. Mišić
The recent advances of emerging technologies including artificial intelligence, 5G, 6G, extended reality and blockchain promote the rapid development of next-generation Internet. As an evolving paradigm of next-generation Internet, metaverse, a fully immersive, hyper spatiotemporal and selfsustaining virtual shared space, is moving from imagination to the coming reality. However, its massive data flow, pervasive user profiling activities and other intrinsic features can lead to a lot of security and privacy concerns, which will hinder its further deployment. Specially, since the identities of users/avatars in the metaverse can be illegally stolen, impersonated, and interoperability issues can be encountered in authentication across metaverses, this paper designs a lightweight and privacy-preserving seamless cross-metaverse authentication and key agreement scheme named MetaverseAKA to meet the challenges. Metaverse-AKA can not only realize the seamless cross-metaverse authentication but also assure the users’ privacy by achieving the anonymity and unlinkability. In addition, Metaverse-AKA also has the following advantages: (i) Realizing the traceability for users in physical world. (ii) Resistance to multiple attacks like impersonation attack, man-in-the-middle attack and replay attack. (iii) Adopting lightweight cryptographic prinitives and having better performance through experiment verification and comparison.
{"title":"Metaverse-AKA: A Lightweight and PrivacyPreserving Seamless Cross-Metaverse Authentication and Key Agreement Scheme","authors":"Yingying Yao, Xiaolin Chang, Lin Li, Jiqiang Liu, J. Misic, V. Mišić","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00340","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00340","url":null,"abstract":"The recent advances of emerging technologies including artificial intelligence, 5G, 6G, extended reality and blockchain promote the rapid development of next-generation Internet. As an evolving paradigm of next-generation Internet, metaverse, a fully immersive, hyper spatiotemporal and selfsustaining virtual shared space, is moving from imagination to the coming reality. However, its massive data flow, pervasive user profiling activities and other intrinsic features can lead to a lot of security and privacy concerns, which will hinder its further deployment. Specially, since the identities of users/avatars in the metaverse can be illegally stolen, impersonated, and interoperability issues can be encountered in authentication across metaverses, this paper designs a lightweight and privacy-preserving seamless cross-metaverse authentication and key agreement scheme named MetaverseAKA to meet the challenges. Metaverse-AKA can not only realize the seamless cross-metaverse authentication but also assure the users’ privacy by achieving the anonymity and unlinkability. In addition, Metaverse-AKA also has the following advantages: (i) Realizing the traceability for users in physical world. (ii) Resistance to multiple attacks like impersonation attack, man-in-the-middle attack and replay attack. (iii) Adopting lightweight cryptographic prinitives and having better performance through experiment verification and comparison.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"6 1","pages":"2421-2427"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85092428","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The ever-increasing concerns on data security and user privacy have significantly impacted the current centralized mechanism of intelligent systems in bridging private data islands and idle computing resources commonly dispersed at the edge. To resolve that, a novel distributed learning paradigm, called Federated Learning (FL), which can learn a global model in a collaborative and privacy-preserving manner, has been proposed and widely discussed. Furthermore, to tackle the data heterogeneity and model adaptation issues faced by FL, meta-learning starts to be applied together with FL to rapidly train a global model with high generalization. However, since federated meta-learning is still in its infancy to collaborate with participants in synchronous mode, straggler and over-fitting issues may impede its application in ubiquitous intelligence, such as smart health and intelligent transportation. Motivated by this, this paper proposes a novel asynchronous federated meta-learning mechanism, called AFMeta, that can measure the staleness of local models to enhance model aggregation. To the best of our knowledge, AFMeta is the first work studying the asynchronous mode in federated meta-learning. We evaluate AFMeta against state-of-the-art baselines on classification and regression tasks. The results show that it boosts the model performance by 44.23% and reduces the learning time by 86.35%.
{"title":"AFMeta: Asynchronous Federated Meta-learning with Temporally Weighted Aggregation","authors":"Sheng Liu, Haohao Qu, Qiyang Chen, Weitao Jian, Rui Liu, Linlin You","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00100","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00100","url":null,"abstract":"The ever-increasing concerns on data security and user privacy have significantly impacted the current centralized mechanism of intelligent systems in bridging private data islands and idle computing resources commonly dispersed at the edge. To resolve that, a novel distributed learning paradigm, called Federated Learning (FL), which can learn a global model in a collaborative and privacy-preserving manner, has been proposed and widely discussed. Furthermore, to tackle the data heterogeneity and model adaptation issues faced by FL, meta-learning starts to be applied together with FL to rapidly train a global model with high generalization. However, since federated meta-learning is still in its infancy to collaborate with participants in synchronous mode, straggler and over-fitting issues may impede its application in ubiquitous intelligence, such as smart health and intelligent transportation. Motivated by this, this paper proposes a novel asynchronous federated meta-learning mechanism, called AFMeta, that can measure the staleness of local models to enhance model aggregation. To the best of our knowledge, AFMeta is the first work studying the asynchronous mode in federated meta-learning. We evaluate AFMeta against state-of-the-art baselines on classification and regression tasks. The results show that it boosts the model performance by 44.23% and reduces the learning time by 86.35%.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"18 1","pages":"641-648"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83808312","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00048
Fengyuan Zhang, Zhiwen Yu, Yimeng Liu, Helei Cui, Bin Guo
Mobile crowdsensing (MCS) collects sensing data by recruiting task participants to realize large-scale sensing tasks in cities. However, due to the limitations of human activity range and sensing mode, relying only on human participants to achieve this process will lead to sensing blind areas, ultimately affecting the integrity and validity of sensing data. With the rise of unmanned vehicles (UVs) and sensor-assisted MCS research, it provides new inspirations for solving complex sensing tasks in smart cities. In this article, we propose heterogeneous crowdsensing, which includes heterogeneous participants such as human participants, UVs, and fixed sensors. Our goal is to accomplish large-scale, high-quality urban sensing tasks by collaborating with these three types of heterogeneous participants. To solve the collaborative sensing problem, we propose an algorithm called spatio-temporal PPO (STPPO). We first define the capability and cost attributes of the heterogeneous participants and then divide the large-scale sensing area into a set of subregions by a subgraph construction method. Based on the spatio-temporal characteristics of the subregions and the attributes of the heterogeneous participants, we finally solve the cooperative scheduling problem of the subregions using proximal policy optimization (PPO) algorithms to maximize the overall POI collection rate and collection fairness. Finally, extensive experiments are conducted based on real datasets. The overall results of STPPO outperform other baselines, with a 30.19% performance improvement compared to the PPO algorithm.
{"title":"Spatio-temporal Feature Based Multi-participant Recruitment in Heterogeneous Crowdsensing","authors":"Fengyuan Zhang, Zhiwen Yu, Yimeng Liu, Helei Cui, Bin Guo","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00048","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00048","url":null,"abstract":"Mobile crowdsensing (MCS) collects sensing data by recruiting task participants to realize large-scale sensing tasks in cities. However, due to the limitations of human activity range and sensing mode, relying only on human participants to achieve this process will lead to sensing blind areas, ultimately affecting the integrity and validity of sensing data. With the rise of unmanned vehicles (UVs) and sensor-assisted MCS research, it provides new inspirations for solving complex sensing tasks in smart cities. In this article, we propose heterogeneous crowdsensing, which includes heterogeneous participants such as human participants, UVs, and fixed sensors. Our goal is to accomplish large-scale, high-quality urban sensing tasks by collaborating with these three types of heterogeneous participants. To solve the collaborative sensing problem, we propose an algorithm called spatio-temporal PPO (STPPO). We first define the capability and cost attributes of the heterogeneous participants and then divide the large-scale sensing area into a set of subregions by a subgraph construction method. Based on the spatio-temporal characteristics of the subregions and the attributes of the heterogeneous participants, we finally solve the cooperative scheduling problem of the subregions using proximal policy optimization (PPO) algorithms to maximize the overall POI collection rate and collection fairness. Finally, extensive experiments are conducted based on real datasets. The overall results of STPPO outperform other baselines, with a 30.19% performance improvement compared to the PPO algorithm.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"79 1","pages":"161-168"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85235727","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00103
Yunzhe Zhu, Yusong Tan, Xiaoling Li, Qingbo Wu, Xueqin Ning
Neural network models have achieved great success in numerous disciplines in recent years, including image segmentation, object identification, and natural language processing (NLP). Incremental learning in these fields focuses on training models in a continuous data stream. As time goes by, more new data becomes available, and old data may become unavailable owing to resource constraints such as storage. As a result, when new data is continually arriving, the performance of the neural network model on the old data sample sometimes decreases significantly, a phenomenon known as catastrophic forgetting. Many corresponding strategies have been proposed to mitigate the catastrophic forgetting of neural network models, which are based on parameter regularization, data replay, and parameter isolation. This paper proposes an incremental learning method based on data feature distribution (ICFD). The method uses Gaussian distribution to generate features from old data to train neural network models based on the phenomenon that feature vectors obey multi-dimensional Gaussian distribution in feature space. This method avoids storing a large number of original samples, and the generated old class features contain more sample information. This method combines data playback and parameter regularization in concrete implementation. The experimental results of ICFD on the CIFAR-100 demonstrate that when the incremental step is 5, the average incremental accuracy is increased by 10.4%. When the incremental step is 10, the average incremental accuracy is improved by 8.1%.
{"title":"ICFD: An Incremental Learning Method Based on Data Feature Distribution","authors":"Yunzhe Zhu, Yusong Tan, Xiaoling Li, Qingbo Wu, Xueqin Ning","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00103","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00103","url":null,"abstract":"Neural network models have achieved great success in numerous disciplines in recent years, including image segmentation, object identification, and natural language processing (NLP). Incremental learning in these fields focuses on training models in a continuous data stream. As time goes by, more new data becomes available, and old data may become unavailable owing to resource constraints such as storage. As a result, when new data is continually arriving, the performance of the neural network model on the old data sample sometimes decreases significantly, a phenomenon known as catastrophic forgetting. Many corresponding strategies have been proposed to mitigate the catastrophic forgetting of neural network models, which are based on parameter regularization, data replay, and parameter isolation. This paper proposes an incremental learning method based on data feature distribution (ICFD). The method uses Gaussian distribution to generate features from old data to train neural network models based on the phenomenon that feature vectors obey multi-dimensional Gaussian distribution in feature space. This method avoids storing a large number of original samples, and the generated old class features contain more sample information. This method combines data playback and parameter regularization in concrete implementation. The experimental results of ICFD on the CIFAR-100 demonstrate that when the incremental step is 5, the average incremental accuracy is increased by 10.4%. When the incremental step is 10, the average incremental accuracy is improved by 8.1%.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"35 1","pages":"618-626"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85817338","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00354
Hairuo Xu, Tao Shu
The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense.
{"title":"Attack-Model-Agnostic Defense Against Model Poisonings in Distributed Learning","authors":"Hairuo Xu, Tao Shu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00354","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00354","url":null,"abstract":"The distributed nature of distributed learning renders the learning process susceptible to model poisoning attacks. Most existing countermeasures are designed based on a presumed attack model, and can only perform under the presumed attack model. However, in reality a distributed learning system typically does not have the luxury of knowing the attack model it is going to be actually facing in its operation when the learning system is deployed, thus constituting a zero-day vulnerability of the system that has been largely overlooked so far. In this paper, we study the attack-model-agnostic defense mechanisms for distributed learning, which are capable of countering a wide-spectrum of model poisoning attacks without relying on assumptions of the specific attack model, and hence alleviating the zero-day vulnerability of the system. Extensive experiments are performed to verify the effectiveness of the proposed defense.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"48 1","pages":"1515-1522"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82139024","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00156
Shaolong Zheng, Zewei Xu, Zhenni Li, Yihui Cai, Mingyu Han, Yi Ji
It is very important for art students to get timely feedback on their paintings. Currently, this work is done by professional teachers. However, it is problematic for the scoring method since the subjectivity of manual scoring and the scarcity of teacher resources. It is time-consuming and expensive to carry out this work in practice. In this paper, we propose a depthwise separable convolutional network with multi-head self-attention module (DCMnet) for developing an intelligent scoring mechanism for sketch portraits. Specifically, to build a lightweight network, we first utilize the depthwise separable convolutional block as the backbone of the model for mining the local features of sketch portraits. Then the attention module is employed to notice global dependencies within internal representations of portraits. Finally, we use DCMnet to build a scoring framework, which first divides the works into four score levels, and then subdivides them into eight grades: below 60, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, and above 90. Each grade of work is given a basic score, and the final score of works is composed of the basic score and the mood factor. In the training process, a pretraining strategy is introduced for fast convergence. For verifying our method, we collect a sketch portrait dataset in the Guangdong Fine Arts Joint Examination to train the DCMnet. The experimental results demonstrate that the proposed method achieves excellent accuracy at each grade and the efficiency of scoring is improved.
{"title":"An Intelligent Scoring Method for Sketch Portrait Based on Attention Convolution Neural Network","authors":"Shaolong Zheng, Zewei Xu, Zhenni Li, Yihui Cai, Mingyu Han, Yi Ji","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00156","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00156","url":null,"abstract":"It is very important for art students to get timely feedback on their paintings. Currently, this work is done by professional teachers. However, it is problematic for the scoring method since the subjectivity of manual scoring and the scarcity of teacher resources. It is time-consuming and expensive to carry out this work in practice. In this paper, we propose a depthwise separable convolutional network with multi-head self-attention module (DCMnet) for developing an intelligent scoring mechanism for sketch portraits. Specifically, to build a lightweight network, we first utilize the depthwise separable convolutional block as the backbone of the model for mining the local features of sketch portraits. Then the attention module is employed to notice global dependencies within internal representations of portraits. Finally, we use DCMnet to build a scoring framework, which first divides the works into four score levels, and then subdivides them into eight grades: below 60, 60-64, 65-69, 70-74, 75-79, 80-84, 85-89, and above 90. Each grade of work is given a basic score, and the final score of works is composed of the basic score and the mood factor. In the training process, a pretraining strategy is introduced for fast convergence. For verifying our method, we collect a sketch portrait dataset in the Guangdong Fine Arts Joint Examination to train the DCMnet. The experimental results demonstrate that the proposed method achieves excellent accuracy at each grade and the efficiency of scoring is improved.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"6 1","pages":"1058-1064"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82515561","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00363
Song Wu, Xiaojiang Zhang, Wei Dong, Senzhang Wang, Xiaoyong Li, Senliang Bao, K. Li
Accurately predicting the occurrence of oceanic internal waves in the northeastern South China Sea is of great importance to marine ecosystems, and economy. The traditional physics-based models for monitoring the occurrence of internal waves require complex parameterization, and the partial differential equations (PDEs) are relatively difficult to solve. The emergence of integrating physical knowledge and data-driven models brings light to solving the problem, which improves interpretability and meets the physical consistency. It not only inherits the advantages of machine learning in massive data processing but also makes up for the “black box” characteristics. In this paper, we propose a physics-based spatio-temporal data analysis model based on the widely used LSTM framework to achieve oceanic internal wave prediction. The results show higher prediction accuracy compared with the traditional LSTM model, and the introduction of physical laws can improve data utilization while enhancing interpretability.
{"title":"Physics-Based Spatio-Temporal Modeling With Machine Learning for the Prediction of Oceanic Internal Waves","authors":"Song Wu, Xiaojiang Zhang, Wei Dong, Senzhang Wang, Xiaoyong Li, Senliang Bao, K. Li","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00363","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00363","url":null,"abstract":"Accurately predicting the occurrence of oceanic internal waves in the northeastern South China Sea is of great importance to marine ecosystems, and economy. The traditional physics-based models for monitoring the occurrence of internal waves require complex parameterization, and the partial differential equations (PDEs) are relatively difficult to solve. The emergence of integrating physical knowledge and data-driven models brings light to solving the problem, which improves interpretability and meets the physical consistency. It not only inherits the advantages of machine learning in massive data processing but also makes up for the “black box” characteristics. In this paper, we propose a physics-based spatio-temporal data analysis model based on the widely used LSTM framework to achieve oceanic internal wave prediction. The results show higher prediction accuracy compared with the traditional LSTM model, and the introduction of physical laws can improve data utilization while enhancing interpretability.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"37 1","pages":"604-609"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79724037","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Electronic registration identification technology (ERI) has developed rapidly in recent years. This technology has been widely used in large urban transportation monitoring, vehicle counting, identification, and traffic congestion detection. It has many advantages, such as long recognition distance, high recognition accuracy, more information stored, fast reading speed, etc. Currently this technology has achieved full coverage of the entire road network and vehicles in the cities where it is applied. Despite the richness of this data, there are significant limitations in terms of vehicle trajectories, especially in terms of spatial and temporal density. Compared with ERI trajectories, vehicle GPS trajectories have a higher sampling rate, but we are unable to obtain more comprehensive and complete vehicle GPS data due to the limitations of vehicle technology and security factors. In this paper, we innovatively propose a new method to reconstruct fine-grained ERI trajectories by learning from taxi GPS data. This approach can be divided into two steps. First, a novel Taxi-ERI traffic network is proposed to connect ERI data and taxi data. It’s a directed multi-graph whose nodes are consisted of all ERI acquisition points and edges are composed of clustered taxi trajectories. Then, the probability of each road is calculated by a Bayes classification based on the multi-road travel time distribution model while there are multi roads between two adjacent acquisition points, the model parameters are trained by the expectation maximization (EM) algorithm. Finally, we extensively evaluate the proposed framework on the taxi trajectory dataset and ERI data collected from Chongqing, China. The experimental results show that the method can accurately reconstruct vehicle trajectories.
{"title":"Fine-grained Reconstruction of Vehicle Trajectories Based on Electronic Registration Identification Data","authors":"Xin Chen, Linjiang Zheng, Wengang Li, Longquan Liao, Qixing Wang, Xingze Yang","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00065","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00065","url":null,"abstract":"Electronic registration identification technology (ERI) has developed rapidly in recent years. This technology has been widely used in large urban transportation monitoring, vehicle counting, identification, and traffic congestion detection. It has many advantages, such as long recognition distance, high recognition accuracy, more information stored, fast reading speed, etc. Currently this technology has achieved full coverage of the entire road network and vehicles in the cities where it is applied. Despite the richness of this data, there are significant limitations in terms of vehicle trajectories, especially in terms of spatial and temporal density. Compared with ERI trajectories, vehicle GPS trajectories have a higher sampling rate, but we are unable to obtain more comprehensive and complete vehicle GPS data due to the limitations of vehicle technology and security factors. In this paper, we innovatively propose a new method to reconstruct fine-grained ERI trajectories by learning from taxi GPS data. This approach can be divided into two steps. First, a novel Taxi-ERI traffic network is proposed to connect ERI data and taxi data. It’s a directed multi-graph whose nodes are consisted of all ERI acquisition points and edges are composed of clustered taxi trajectories. Then, the probability of each road is calculated by a Bayes classification based on the multi-road travel time distribution model while there are multi roads between two adjacent acquisition points, the model parameters are trained by the expectation maximization (EM) algorithm. Finally, we extensively evaluate the proposed framework on the taxi trajectory dataset and ERI data collected from Chongqing, China. The experimental results show that the method can accurately reconstruct vehicle trajectories.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"14 1","pages":"301-309"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80500193","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00077
Jingjie Wang, Xiang Wei, Siyang Lu, Mingquan Wang, Xiaoyu Liu, Wei Lu
Nowadays, the self-attention mechanism has become a resound of visual feature extraction along with convolution. The transformer network composed of self-attention has developed rapidly and made remarkable achievements in visual tasks. The self-attention shows the potential to replace convolution as the primary method of visual feature extraction in ubiquitous intelligence. Nevertheless, the development of the Visual Transformer still suffer from the following problems: a) The self-attention mechanism has a low inductive bias, which leads to large data demand and a high training cost. b) The Transformer backbone network cannot adapt well to the low visual information density and performs unsatisfactorily under low resolution and small-scale datasets. To tackle the abovementioned two problems, this paper proposes a novel algorithm based on the mature Visual Transformer architecture, which is dedicated to exploring the performance potential of the Transformer network and its kernel self-attention mechanism on small-scale datasets. Specifically, we first propose a network architecture equipped with multi-coordination strategy to solve the self-attention degradation problem inherent in the existing Transformer architecture. Secondly, we introduce consistent regularization into the Transformer to make the self-attention mechanism acquire more reliable feature representation ability in the case of insufficient visual features. In the experiments, CSwin Transformer, the mainstream visual model, is selected to verify the effectiveness of the proposed method on the prevalent small datasets, and superior results are achieved. In particular, without pre-training, our accuracy on the CIFAR-100 dataset is improved by 1.24% compared to CSwin.
{"title":"Redesign Visual Transformer For Small Datasets","authors":"Jingjie Wang, Xiang Wei, Siyang Lu, Mingquan Wang, Xiaoyu Liu, Wei Lu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00077","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00077","url":null,"abstract":"Nowadays, the self-attention mechanism has become a resound of visual feature extraction along with convolution. The transformer network composed of self-attention has developed rapidly and made remarkable achievements in visual tasks. The self-attention shows the potential to replace convolution as the primary method of visual feature extraction in ubiquitous intelligence. Nevertheless, the development of the Visual Transformer still suffer from the following problems: a) The self-attention mechanism has a low inductive bias, which leads to large data demand and a high training cost. b) The Transformer backbone network cannot adapt well to the low visual information density and performs unsatisfactorily under low resolution and small-scale datasets. To tackle the abovementioned two problems, this paper proposes a novel algorithm based on the mature Visual Transformer architecture, which is dedicated to exploring the performance potential of the Transformer network and its kernel self-attention mechanism on small-scale datasets. Specifically, we first propose a network architecture equipped with multi-coordination strategy to solve the self-attention degradation problem inherent in the existing Transformer architecture. Secondly, we introduce consistent regularization into the Transformer to make the self-attention mechanism acquire more reliable feature representation ability in the case of insufficient visual features. In the experiments, CSwin Transformer, the mainstream visual model, is selected to verify the effectiveness of the proposed method on the prevalent small datasets, and superior results are achieved. In particular, without pre-training, our accuracy on the CIFAR-100 dataset is improved by 1.24% compared to CSwin.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"28 1","pages":"401-408"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80934160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00051
Wentong Li, Yina Lv, Changlong Li, Liang Shi
With the rapid development of mobile devices, remote swapping has been widely studied across mobile devices. However, one challenge for remote swapping is its unsatisfying user experience. This is because remote swapping always requires a large amount of data swapping across devices. In this work, an access characteristic guided remote swapping scheme, ACR-Swap, is proposed to optimize user experience. This work is motivated by observations from our comprehensive studies on the access characteristics of existing remote swapping. First, the swap-in operations of system service processes are more frequent than that of the application-specific processes. Second, apps have a different amount of swap-in operations in different running periods. Based on the observations, ACR-Swap is designed with two schemes to optimize the remote swapping. First, a process-aware page sifting (PPS) scheme is designed to identify processes and determine data placement across devices. Second, an adaptive-granularity prefetching (AGP) scheme is proposed to prefetch data across devices based on the running period of apps. ACR-Swap is demonstrated on real mobile devices. Experimental results show that ACR-Swap can significantly reduce the app switching latency compared with the state-of-the-arts and improves the app caching capability, compared to no swapping.
{"title":"Access Characteristic Guided Remote Swapping for User Experience Optimization on Mobile Devices","authors":"Wentong Li, Yina Lv, Changlong Li, Liang Shi","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00051","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00051","url":null,"abstract":"With the rapid development of mobile devices, remote swapping has been widely studied across mobile devices. However, one challenge for remote swapping is its unsatisfying user experience. This is because remote swapping always requires a large amount of data swapping across devices. In this work, an access characteristic guided remote swapping scheme, ACR-Swap, is proposed to optimize user experience. This work is motivated by observations from our comprehensive studies on the access characteristics of existing remote swapping. First, the swap-in operations of system service processes are more frequent than that of the application-specific processes. Second, apps have a different amount of swap-in operations in different running periods. Based on the observations, ACR-Swap is designed with two schemes to optimize the remote swapping. First, a process-aware page sifting (PPS) scheme is designed to identify processes and determine data placement across devices. Second, an adaptive-granularity prefetching (AGP) scheme is proposed to prefetch data across devices based on the running period of apps. ACR-Swap is demonstrated on real mobile devices. Experimental results show that ACR-Swap can significantly reduce the app switching latency compared with the state-of-the-arts and improves the app caching capability, compared to no swapping.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"69 1","pages":"186-193"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81200938","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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