Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00170
Shuqin Zhu, Xiaoping Che, Chenxin Qu, Hao Li, Siyuan Wang
Virtual reality (VR) has become popular recently due to the release of several consumer-grade VR devices. At present, VR technology has been widely used in education, entertainment, and other fields. However, up to now, there is no unified method to teach users how to interact in the virtual environment. This work explores the impact of guidance forms on user experience and basic operations in virtual environments based on three VR games with interactive content ranging from simple to complex. We conducted a user study (n=105) to compare the impact of text-and-image-based guidance, video-based guidance, and interactive guidance on user experience and basic operational learning. The results show that interactive guidance makes users more immersive, especially in environments that involve complex interactions. At the same time, the user’s sense of immersion, which is reflected in the heart rate change in this study, is also significantly correlated with the user’s learning situation and can be used as an indicator to roughly estimate the user’s learning situation. These findings emphasize that user-guidance forms, as an important part of the virtual reality experience, can directly affect the user experience and the user’s learning of operations in the virtual environment.
{"title":"Which User Guidance Works Better in VR? A User Guidance Learning Effect Study in Virtual Environment","authors":"Shuqin Zhu, Xiaoping Che, Chenxin Qu, Hao Li, Siyuan Wang","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00170","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00170","url":null,"abstract":"Virtual reality (VR) has become popular recently due to the release of several consumer-grade VR devices. At present, VR technology has been widely used in education, entertainment, and other fields. However, up to now, there is no unified method to teach users how to interact in the virtual environment. This work explores the impact of guidance forms on user experience and basic operations in virtual environments based on three VR games with interactive content ranging from simple to complex. We conducted a user study (n=105) to compare the impact of text-and-image-based guidance, video-based guidance, and interactive guidance on user experience and basic operational learning. The results show that interactive guidance makes users more immersive, especially in environments that involve complex interactions. At the same time, the user’s sense of immersion, which is reflected in the heart rate change in this study, is also significantly correlated with the user’s learning situation and can be used as an indicator to roughly estimate the user’s learning situation. These findings emphasize that user-guidance forms, as an important part of the virtual reality experience, can directly affect the user experience and the user’s learning of operations in the virtual environment.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"69 1","pages":"1178-1183"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80280054","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 goal of few-shot semantic segmentation (FSS) is to segment the foreground image of an unseen class in the query image by using a few labeled support images. Existing two-branch models mine support and query image information to improve segmentation results by employing support prototypes, calculating the similarity between support and query images, or fusing multi-scale features. Such methods only focus on the spatial information of the query image in the initial feature extraction and subsequent processes. Meanwhile, limited by the sample size, their ability to extract channel information is insufficient, thus leading to the information loss of the query image. To solve the issues, we propose an implicit channel relation based few-shot semantic segmentation method entitled MANGO. The implicit relation mining process is implemented after the initial feature extraction and before the two branches interact to fully mine the query image information. Specifically, the query channel features are taken as nodes to construct the graph structure to establish the relationship between nodes. The network motif is used to quantity the attribute features and structural features of nodes to enhance the relationship between channels. Finally, we aggregate the two features and mine the implicit relationship of nodes through graph representation learning. Experiments on PASCAL-5i and FSS-1000 datasets demonstrate that our proposed method outperforms the state-of-the-art methods.
{"title":"Mining Implicit Relations Among Image Channels for Few-Shot Semantic Segmentation","authors":"Xu Yuan, Ying Yang, Huafei Huang, Shuo Yu, Lili Cong","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00062","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00062","url":null,"abstract":"The goal of few-shot semantic segmentation (FSS) is to segment the foreground image of an unseen class in the query image by using a few labeled support images. Existing two-branch models mine support and query image information to improve segmentation results by employing support prototypes, calculating the similarity between support and query images, or fusing multi-scale features. Such methods only focus on the spatial information of the query image in the initial feature extraction and subsequent processes. Meanwhile, limited by the sample size, their ability to extract channel information is insufficient, thus leading to the information loss of the query image. To solve the issues, we propose an implicit channel relation based few-shot semantic segmentation method entitled MANGO. The implicit relation mining process is implemented after the initial feature extraction and before the two branches interact to fully mine the query image information. Specifically, the query channel features are taken as nodes to construct the graph structure to establish the relationship between nodes. The network motif is used to quantity the attribute features and structural features of nodes to enhance the relationship between channels. Finally, we aggregate the two features and mine the implicit relationship of nodes through graph representation learning. Experiments on PASCAL-5i and FSS-1000 datasets demonstrate that our proposed method outperforms the state-of-the-art methods.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"13 1","pages":"275-284"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79406077","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-Metaverse56740.2022.00240
Shaopeng Wang, Chunkai Feng
Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN.
{"title":"Discovering All-chain Set with Direction and Graduality Characteristics over Streaming Time Series","authors":"Shaopeng Wang, Chunkai Feng","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00240","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00240","url":null,"abstract":"Since its introduction over five years ago, time series chain has become a fundamental tool for time series analytics, finding diverse uses in dozens of domains. Recent work has generalized the definition of time series chain, and introduced a novel definition of time series chain with directionality and graduality characteristics (TSC-DG) which can significantly enhance both robustness and usability of the original time series chain. However, previous studies on TSCDG process fixed-length time series. In this work, we focus on the issue of all-chain set with direction and graduality characteristics (all-TSCS-DG) mining over streaming time series for the first time, where all-TSCS-DG is the core of current TSCDG researches. We propose an improved Naive algorithm (IN) to solve this problem. Compared to the Naive, the IN guarantees the same space costs and results firstly, secondly is the IN takes two additional optimal strategies to further improve the time efficiency. The basic ideas of these two strategies are both incremental computing. The first one can make the IN update the IB structure at each time-tick incrementally, where the IB is an important data structure that is used to obtain the all-TSCS-DG. The second one makes the IN obtain mining results at current time-tick based on the ones at the last time-tick incrementally. Extensive experiments on real dataset demonstrate the efficiency and effectiveness of the IN.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"7 1","pages":"1664-1670"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79525821","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-Metaverse56740.2022.00372
Fadi Alhaddadin, Jairo Gutiérrez
The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients’ information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients’ health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients’ information before releasing it for research purposes according to certain requirements specified by the data user (researcher).
{"title":"Utility-Aware Data Anonymization Model for Healthcare Information","authors":"Fadi Alhaddadin, Jairo Gutiérrez","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00372","url":null,"abstract":"The use of collected data is a valuable source for analysis that benefits both medical research and practice. Information privacy is considered a significant challenge that hinders using such information for research purposes. In terms of research, releasing patients’ information for research purposes may lead to privacy breaches for patients in various cases. Individual patients may not wish to be identifiable when using information about their health for research. This work proposes a utility-aware data anonymization model for sharing patients’ health information for research purposes in a privacy-preserving manner. The proposed model is interactive and involves a number of operations that are performed on patients’ information before releasing it for research purposes according to certain requirements specified by the data user (researcher).","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"93 1","pages":"2348-2357"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78598611","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-Metaverse56740.2022.00179
Xue Li, Junjie Zhang, Junlong Ma
Multiple Choice Question Answering(MCQA) aims to automatically choose a correct answer from candidate options when given a passage and question. Existing approaches generally model attention mechanisms based on whole-passage information or manually tag key sentences for weakly supervised learning, which leads to the models focusing extensively on redundant information and costly manual annotation. In this paper, we consider evidence sentence extraction work in an unsupervised way to precisely pinpoint evidence sentences and minimize the impact of redundant information while avoiding costly manual annotations. Specifically, we propose a novel model called Term Similarity-aware Extensive and Intensive Reading(TS-EIR), which dynamically and automatically refines critical information by term similarity. In detail, it intelligently selects sentences more relevant to the question from the passage and deeply extracts features by enhanced graph convolutional neural network. We apply the proposed TS-EIR to a typical pre-trained language model, BERT, for encoding and evaluate it on the RACE and Dream benchmarks, which verify our model achieves substantial performance improvements over the current baseline.
{"title":"Term Similarity-aware Extensive and Intensive Reading For Multiple Choice Question Answering","authors":"Xue Li, Junjie Zhang, Junlong Ma","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00179","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00179","url":null,"abstract":"Multiple Choice Question Answering(MCQA) aims to automatically choose a correct answer from candidate options when given a passage and question. Existing approaches generally model attention mechanisms based on whole-passage information or manually tag key sentences for weakly supervised learning, which leads to the models focusing extensively on redundant information and costly manual annotation. In this paper, we consider evidence sentence extraction work in an unsupervised way to precisely pinpoint evidence sentences and minimize the impact of redundant information while avoiding costly manual annotations. Specifically, we propose a novel model called Term Similarity-aware Extensive and Intensive Reading(TS-EIR), which dynamically and automatically refines critical information by term similarity. In detail, it intelligently selects sentences more relevant to the question from the passage and deeply extracts features by enhanced graph convolutional neural network. We apply the proposed TS-EIR to a typical pre-trained language model, BERT, for encoding and evaluate it on the RACE and Dream benchmarks, which verify our model achieves substantial performance improvements over the current baseline.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"15 1","pages":"1285-1291"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76895686","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-Metaverse56740.2022.00274
Ting Li, Chundong Wang, Huai-bin Wang
In recent years, user-user trust relationships have played an important role in recommendation based on graph neural networks(GNNs). However, existing studies based on GNNs still face the following challenges: how to obtain more rating information of users’ trust from trust networks when using GNNs to learn the user latent feature. And how to effectively mine items’ relationships from the recommended data so that GNNs can better learn the item latent feature. To address the above challenges, in this paper, we propose a new model called TGNRec that accomplishes recommendation based on trust networks and graph neural networks. TGNRec consists of three modules: User Spatial Module, Item Spatial Module, Prediction Module. User Spatial Module considers both the rating information of users’ direct and indirect trust based on the transfer properties of trust relationships in trust networks. It mainly learns the user latent feature using user-item interactions and user-user trust relationships. Item Spatial Module establishes items’ similarity relationships based on the rating mean, which helps GNNs learn the item latent feature from user-item interactions and item-item relationships. Prediction Module realizes users’ rating prediction for unrated items by aggregating User Spatial Module and Item Spatial Module. At last, we conduct experiments on two real-world datasets, Film Trust and Ciao-DVD. The experimental results demonstrate the effectiveness of TGNRec for rating prediction in recommendation.
{"title":"TGNRec: Recommendation Based on Trust Networks and Graph Neural Networks","authors":"Ting Li, Chundong Wang, Huai-bin Wang","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00274","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00274","url":null,"abstract":"In recent years, user-user trust relationships have played an important role in recommendation based on graph neural networks(GNNs). However, existing studies based on GNNs still face the following challenges: how to obtain more rating information of users’ trust from trust networks when using GNNs to learn the user latent feature. And how to effectively mine items’ relationships from the recommended data so that GNNs can better learn the item latent feature. To address the above challenges, in this paper, we propose a new model called TGNRec that accomplishes recommendation based on trust networks and graph neural networks. TGNRec consists of three modules: User Spatial Module, Item Spatial Module, Prediction Module. User Spatial Module considers both the rating information of users’ direct and indirect trust based on the transfer properties of trust relationships in trust networks. It mainly learns the user latent feature using user-item interactions and user-user trust relationships. Item Spatial Module establishes items’ similarity relationships based on the rating mean, which helps GNNs learn the item latent feature from user-item interactions and item-item relationships. Prediction Module realizes users’ rating prediction for unrated items by aggregating User Spatial Module and Item Spatial Module. At last, we conduct experiments on two real-world datasets, Film Trust and Ciao-DVD. The experimental results demonstrate the effectiveness of TGNRec for rating prediction in recommendation.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"24 1","pages":"1896-1901"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78854807","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-Metaverse56740.2022.00196
Xin Wang, Hongbin Shi
Multi-hop knowledge base question answering aims to answer natural language questions through multi-hop relation reasoning in the knowledge base. An important challenge of the task is the lack of labels for reasoning paths, which leads to the possibility to produce correct answers through incorrect paths in the training, and cannot generalize well in testing. Recently research has attempted to handle the challenge by devising reward shaping or introducing additional information to generate supervision signals of intermediate paths. But they required extra expert experience and label information. To address this situation, we propose a novel method under the teacher-student framework, it leverages perturbation consistency to learn intermediate paths. In the teacher network, we construct close data points for intermediate path prediction by applying random perturbations. Inspired by the data smoothing assumption that labels of close data points should be the same, a consistency loss over predictions of constructed data points and original ones is evaluated. The student network is used to answer questions more precisely by leveraging the intermediate distribution learned from the teacher network. Extensive experiments on two benchmark datasets are conducted, and the results have demonstrated the effectiveness of the proposed method.
{"title":"Leveraging Perturbation Consistency to Improve Multi-hop Knowledge Base Question Answering","authors":"Xin Wang, Hongbin Shi","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00196","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00196","url":null,"abstract":"Multi-hop knowledge base question answering aims to answer natural language questions through multi-hop relation reasoning in the knowledge base. An important challenge of the task is the lack of labels for reasoning paths, which leads to the possibility to produce correct answers through incorrect paths in the training, and cannot generalize well in testing. Recently research has attempted to handle the challenge by devising reward shaping or introducing additional information to generate supervision signals of intermediate paths. But they required extra expert experience and label information. To address this situation, we propose a novel method under the teacher-student framework, it leverages perturbation consistency to learn intermediate paths. In the teacher network, we construct close data points for intermediate path prediction by applying random perturbations. Inspired by the data smoothing assumption that labels of close data points should be the same, a consistency loss over predictions of constructed data points and original ones is evaluated. The student network is used to answer questions more precisely by leveraging the intermediate distribution learned from the teacher network. Extensive experiments on two benchmark datasets are conducted, and the results have demonstrated the effectiveness of the proposed method.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"25 1","pages":"1360-1365"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76773025","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-Metaverse56740.2022.00280
Chang Liu, Jianxia Chen, Tianci Wang, Qi Liu, Xinyun Wu, Lei Mao
Aspect-level sentiment classification task aims at determining the sentiment polarity towards each aspect in a sentence. Although existing models have achieved remarkable performance, they always ignore the semantic relationship between aspects and their context, resulting in the lack of syntax information and aspect features. Therefore, the paper proposes a novel model named ASC based on the Self-Attention routing combined with the Position-biased weight approach, ASC-SAP in short. First, the paper utilizes the position-biased weight approach to construct an aspect-enhanced embedding. Furthermore, the paper develops a novel non-iterative but highly parallelized self-attention routing mechanism to efficiently transfer the aspect features to the target capsules. In addition, the paper utilizes pre-trained model bidirectional encoder representation from transformers (BERT). Comprehensive experiments show that our model achieves excellent performance on Twitter and SemEval2014 benchmarks and verify the effectiveness of our models.
{"title":"Aspect-Level Sentiment Classification Based on Self-Attention Routing via Capsule Network","authors":"Chang Liu, Jianxia Chen, Tianci Wang, Qi Liu, Xinyun Wu, Lei Mao","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00280","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00280","url":null,"abstract":"Aspect-level sentiment classification task aims at determining the sentiment polarity towards each aspect in a sentence. Although existing models have achieved remarkable performance, they always ignore the semantic relationship between aspects and their context, resulting in the lack of syntax information and aspect features. Therefore, the paper proposes a novel model named ASC based on the Self-Attention routing combined with the Position-biased weight approach, ASC-SAP in short. First, the paper utilizes the position-biased weight approach to construct an aspect-enhanced embedding. Furthermore, the paper develops a novel non-iterative but highly parallelized self-attention routing mechanism to efficiently transfer the aspect features to the target capsules. In addition, the paper utilizes pre-trained model bidirectional encoder representation from transformers (BERT). Comprehensive experiments show that our model achieves excellent performance on Twitter and SemEval2014 benchmarks and verify the effectiveness of our models.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"279 1","pages":"1936-1941"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87015073","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-Metaverse56740.2022.00318
Yi Yang, Wenqiang Ma, Wenqiao Sun, Haibin Zhang t, Zhiqiang Liu, Lexi Xu, Ye Zhu
As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.
{"title":"Privacy-Preserving Digital Twin for Vehicular Edge Computing Networks","authors":"Yi Yang, Wenqiang Ma, Wenqiao Sun, Haibin Zhang t, Zhiqiang Liu, Lexi Xu, Ye Zhu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00318","url":null,"abstract":"As an emerging technology, digital twin (DT) has great potential to address the challenges posed by the dynamics and complexity of vehicles in vehicular edge computing (VEC) networks. By mapping the VEC network to the virtual space, DT can monitor vehicles, road side units (RSUs), channels, and resource usage in real time, further bringing comprehensive and accurate network analysis to the VEC network. However, the real-world implement of DT-empowered VEC networks cannot avoid the collection of privacy-sensitive information of participants. An incentive mechanism is necessitated to identify the qualities of participants without prior information and incent them to participate in DT modeling, so as to realize the requirement of privacy preserving while improving the DT modeling efficiency. In this paper, We propose a combined multi-armed bandit-based auction (CMABA) incentive mechanism that can identify the quality of clients in the VEC network without revealing sensitive and private information, and achieve the optimal performance of the model under budget constraints. The simulation results show that this scheme can significantly incent high-quality clients to participate in DT modeling under the requirement of privacy preserving and the constraint of limited budget, and improve the accuracy of DT modeling.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"23 1","pages":"2238-2243"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84964705","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-Metaverse56740.2022.00327
Feiyang Tang
Biometric data privacy is becoming a major concern for many organizations in the age of big data, particularly in the ICT sector, because it may be easily exploited in apps. Most apps utilize biometrics by accessing common application programming interfaces (APIs); hence, we aim to categorize their usage. The categorization based on behavior may be closely correlated with the sensitive processing of a user’s biometric data, hence highlighting crucial biometric data privacy assessment concerns. We propose PABAU, Privacy Analysis of Biometric API Usage. PABAU learns semantic features of methods in biometric APIs and uses them to detect and categorize the usage of biometric API implementation in the software according to their privacy-related behaviors. This technique bridges the communication and background knowledge gap between technical and non-technical individuals in organizations by providing an automated method for both parties to acquire a rapid understanding of the essential behaviors of biometric API in apps, as well as future support to data protection officers (DPO) with legal documentation, such as conducting a Data Protection Impact Assessment (DPIA).
{"title":"PABAU: Privacy Analysis of Biometric API Usage","authors":"Feiyang Tang","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00327","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00327","url":null,"abstract":"Biometric data privacy is becoming a major concern for many organizations in the age of big data, particularly in the ICT sector, because it may be easily exploited in apps. Most apps utilize biometrics by accessing common application programming interfaces (APIs); hence, we aim to categorize their usage. The categorization based on behavior may be closely correlated with the sensitive processing of a user’s biometric data, hence highlighting crucial biometric data privacy assessment concerns. We propose PABAU, Privacy Analysis of Biometric API Usage. PABAU learns semantic features of methods in biometric APIs and uses them to detect and categorize the usage of biometric API implementation in the software according to their privacy-related behaviors. This technique bridges the communication and background knowledge gap between technical and non-technical individuals in organizations by providing an automated method for both parties to acquire a rapid understanding of the essential behaviors of biometric API in apps, as well as future support to data protection officers (DPO) with legal documentation, such as conducting a Data Protection Impact Assessment (DPIA).","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"5 1","pages":"2301-2308"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85011289","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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