Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00086
Qinglong Peng, Bin Tang, Jinhuan Liu, Shuang Cui, Junwei Du, Yan Lu, Feng Jiang, Xu Yu
Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.
{"title":"A Multi-Head Attention Based Dual Target Graph Collaborative Filtering Network","authors":"Qinglong Peng, Bin Tang, Jinhuan Liu, Shuang Cui, Junwei Du, Yan Lu, Feng Jiang, Xu Yu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00086","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00086","url":null,"abstract":"Recently, cross-domain collaborative filtering (CDCF) has been widely used to solve the data sparsity problem in recommendation systems. Therein, the dual-target cross-domain recommendation becomes a research hotspot, which aims to improve the recommendation performance of both target and source domains. Most existing approaches tend to use fixed weights or self-attention in a single representation space for the bi-directional inter-domain transfer of the user representation. However, a single representation space leads to limited representation capability, which makes the transfer of the user representation coarse-grained and inaccurate. In this paper, Multi-head Attention Based Dual Target Graph Collaborative Filtering Network (MA-DTGCF) is proposed. The core of the model is the bi-directional transfer graph convolution layer, consisting of a graph convolution layer and a bi-directional transfer layer based on a multi-head attention mechanism. The latter can achieve fine-grained and adaptive transfer of user features in multiple representation subspaces. It is worth noting that by stacking multiple bi-directional transfer graph convolutional layers, we can get high-order user and item features and achieve adaptive transfer of each order user features. Experimental results on three real datasets show that the proposed MA-DTGCF model significantly outperforms the state-of-the-art models in terms of HR and NDCG.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74476078","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}
Service recommendation is important in creating composite services, workflows, e-business solutions, etc. It often takes developers a long time to Figure out what the next service is. A lot of researchers have used collaborative filtering-based or content-based approaches to recommend services for developers. However, failing to model the co-occurrence relationships between services, current approaches cannot recommend the next services for service composition. This leads to a decrease in the accuracy of service composition recommendations. To tackle this problem, this paper proposes an Encoder-Decoder-based Recommender named EDeR, which transforms the service recommendation problem into a generation problem. First, we employ a self-supervised graph embedding method to fully learn the representation of each service according to both structural and descriptive information. Then, based on the co-occurrence relationships between services, we propose an encoder-decoder model to sequentially recommend services in a way that translates user requirements into a composite service. The results obtained from experiments conducted on a real-world dataset show that EDeR outperforms the state-of-the-art approaches significantly.
{"title":"What Is Next? A Generative Approach for Service Composition Recommendations","authors":"Guodong Fan, Shizhan Chen, Hongyue Wu, Ming Zhu, Xiao Xue, Zhiyong Feng","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00078","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00078","url":null,"abstract":"Service recommendation is important in creating composite services, workflows, e-business solutions, etc. It often takes developers a long time to Figure out what the next service is. A lot of researchers have used collaborative filtering-based or content-based approaches to recommend services for developers. However, failing to model the co-occurrence relationships between services, current approaches cannot recommend the next services for service composition. This leads to a decrease in the accuracy of service composition recommendations. To tackle this problem, this paper proposes an Encoder-Decoder-based Recommender named EDeR, which transforms the service recommendation problem into a generation problem. First, we employ a self-supervised graph embedding method to fully learn the representation of each service according to both structural and descriptive information. Then, based on the co-occurrence relationships between services, we propose an encoder-decoder model to sequentially recommend services in a way that translates user requirements into a composite service. The results obtained from experiments conducted on a real-world dataset show that EDeR outperforms the state-of-the-art approaches significantly.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74614627","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-Metaverse56740.2022.00265
Hao Li, P. Yue, Shangcheng Li, Fangqiang Yu, Chenxiao Zhang, Can Yang, Liangcun Jiang
Next Point-of-Interest (POI) recommendation has been applied by many Internet companies to enhance user travel experience. The state-of-the-art deep learning methods in next POI recommendation advocate the self-attention mechanism to model the user long-term check-in sequence. However, the existing methods ignore the interdependence between POI and POI category in the historical interaction. The POI and POI category sequences can be regarded as multi-view information of user check-in behaviors. This paper proposes a multi-view self-attention network (MVSAN) for next POI recommendation. Firstly, MVSAN uses a self-attention layer to update the feature representation of POI and POI category respectively. Then it generates the importance of POI under the condition of the POI category through a co-attention module. To make better use of geospatial information, we design a spatial candidate set filtering module to help the model improve recommendation performance. Experiments on two real check-in datasets show that MVSAN yields outstanding improvements over the state-of-the-art models in terms of recall.
{"title":"Multi-view Self-attention Network for Next POI Recommendation","authors":"Hao Li, P. Yue, Shangcheng Li, Fangqiang Yu, Chenxiao Zhang, Can Yang, Liangcun Jiang","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00265","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00265","url":null,"abstract":"Next Point-of-Interest (POI) recommendation has been applied by many Internet companies to enhance user travel experience. The state-of-the-art deep learning methods in next POI recommendation advocate the self-attention mechanism to model the user long-term check-in sequence. However, the existing methods ignore the interdependence between POI and POI category in the historical interaction. The POI and POI category sequences can be regarded as multi-view information of user check-in behaviors. This paper proposes a multi-view self-attention network (MVSAN) for next POI recommendation. Firstly, MVSAN uses a self-attention layer to update the feature representation of POI and POI category respectively. Then it generates the importance of POI under the condition of the POI category through a co-attention module. To make better use of geospatial information, we design a spatial candidate set filtering module to help the model improve recommendation performance. Experiments on two real check-in datasets show that MVSAN yields outstanding improvements over the state-of-the-art models in terms of recall.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74654920","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-Metaverse56740.2022.00271
Cheng Liang, Yi He, Teng Huang, Di Wu
The deep neural network-based (DNN-based) model has proven powerful in user data behavior representation, efficiently implementing a recommender system (RS). Most prior works focus on developing a sophisticated architecture to better-fit user data. However, user behavior data are commonly collected from multiple scenarios and generated by numerous users, resulting in various biases existing in these data. Unfortunately, prior DNN-based RSs dealing with these biases are fragmented and lack a comprehensive solution. This paper aims to comprehensively handle these biases in user behavior data in preprocessing stage and training state. By incorporating the preprocessing bias (PB) and training bias (TB) into the representative autoencoder-based AutoRec model, we proposed AutoRec++. Experimental results in five commonly used benchmark datasets demonstrate that: 1) the basic model’s preference can boost by the optimal PB and TB combinations, and 2) our proposed AutoRec++ reaches a better prediction accuracy than DNN-based and non-DNN-based state-of-the-art models.
{"title":"AutoRec++: Incorporating Debias Methods into Autoencoder-based Recommender System","authors":"Cheng Liang, Yi He, Teng Huang, Di Wu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00271","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00271","url":null,"abstract":"The deep neural network-based (DNN-based) model has proven powerful in user data behavior representation, efficiently implementing a recommender system (RS). Most prior works focus on developing a sophisticated architecture to better-fit user data. However, user behavior data are commonly collected from multiple scenarios and generated by numerous users, resulting in various biases existing in these data. Unfortunately, prior DNN-based RSs dealing with these biases are fragmented and lack a comprehensive solution. This paper aims to comprehensively handle these biases in user behavior data in preprocessing stage and training state. By incorporating the preprocessing bias (PB) and training bias (TB) into the representative autoencoder-based AutoRec model, we proposed AutoRec++. Experimental results in five commonly used benchmark datasets demonstrate that: 1) the basic model’s preference can boost by the optimal PB and TB combinations, and 2) our proposed AutoRec++ reaches a better prediction accuracy than DNN-based and non-DNN-based state-of-the-art models.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74253717","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-Metaverse56740.2022.00221
Huaicheng Fang, Fuqing Zhu, Jizhong Han, Songlin Hu
A large amount of hateful speech exist on the Internet in the form of text and images uploaded by social media users. Recently, multimodal hateful speech detection task has attracted more and more researchers to invest, producing some representative work for perceiving the negative samples. For this special multimodal task, the ability of multimodal semantic information understanding is particularly crucial. However, the existing models have insufficient understanding ability of image modality semantic compared with the text modality, due to the appearance complexity of each image. Therefore, this paper utilizes the text modality which is well understood by the model to improve understanding ability of image modality semantic. Specifically, this paper proposes an image caption supervision (ICS) auxiliary method for multimodal hateful speech detection, where the image caption is designed to supervise the feature learning of images for further understanding the semantic information. On the Facebook Hateful Memes dataset, the proposed ICS method outperforms some state-of-the-art unimodal and multimodal baselines, demonstrating the effectiveness of ICS.
{"title":"Multimodal Hateful Memes Detection via Image Caption Supervision","authors":"Huaicheng Fang, Fuqing Zhu, Jizhong Han, Songlin Hu","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00221","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00221","url":null,"abstract":"A large amount of hateful speech exist on the Internet in the form of text and images uploaded by social media users. Recently, multimodal hateful speech detection task has attracted more and more researchers to invest, producing some representative work for perceiving the negative samples. For this special multimodal task, the ability of multimodal semantic information understanding is particularly crucial. However, the existing models have insufficient understanding ability of image modality semantic compared with the text modality, due to the appearance complexity of each image. Therefore, this paper utilizes the text modality which is well understood by the model to improve understanding ability of image modality semantic. Specifically, this paper proposes an image caption supervision (ICS) auxiliary method for multimodal hateful speech detection, where the image caption is designed to supervise the feature learning of images for further understanding the semantic information. On the Facebook Hateful Memes dataset, the proposed ICS method outperforms some state-of-the-art unimodal and multimodal baselines, demonstrating the effectiveness of ICS.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72471719","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-Metaverse56740.2022.00195
L. Weigang, L. Martins, Nikson Ferreira, Christian Miranda, Lucas S. Althoff, Walner Pessoa, Mylène C. Q. Farias, Ricardo Jacobi, Mauricio Rincon
Few-shot learning is an important mechanism to minimize the need for the labeling of large amounts of data and taking advantage of transfer learning. To identify image/text input with duality property, this research proposes a “Heuristic once learning (HOL)” mechanism to investigate multi-modal input processing similar to human-like behavior. First, we create an image/text data set of big Latin letters composed of small letters and another data set composed of Arabic, Chinese and Roman numerals. Secondly, we use Convolutional Neural Networks (CNN) for pre-training the dataset of letters to get structural features. Thirdly, using the acquired knowledge, a Self-organizing Map (SOM) and Contrastive Language-Image Pretraining (CLIP) are tested separately using zero-shot learning. Siamese Networks and Vision Transformer (ViT) are also tested using one-shot learning by knowledge transfer to identify the features of unknown characters. The research results show the potential and challenges to realize HOL and make a useful attempt for the development of general agents.
{"title":"Heuristic Once Learning for Image & Text Duality Information Processing","authors":"L. Weigang, L. Martins, Nikson Ferreira, Christian Miranda, Lucas S. Althoff, Walner Pessoa, Mylène C. Q. Farias, Ricardo Jacobi, Mauricio Rincon","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00195","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00195","url":null,"abstract":"Few-shot learning is an important mechanism to minimize the need for the labeling of large amounts of data and taking advantage of transfer learning. To identify image/text input with duality property, this research proposes a “Heuristic once learning (HOL)” mechanism to investigate multi-modal input processing similar to human-like behavior. First, we create an image/text data set of big Latin letters composed of small letters and another data set composed of Arabic, Chinese and Roman numerals. Secondly, we use Convolutional Neural Networks (CNN) for pre-training the dataset of letters to get structural features. Thirdly, using the acquired knowledge, a Self-organizing Map (SOM) and Contrastive Language-Image Pretraining (CLIP) are tested separately using zero-shot learning. Siamese Networks and Vision Transformer (ViT) are also tested using one-shot learning by knowledge transfer to identify the features of unknown characters. The research results show the potential and challenges to realize HOL and make a useful attempt for the development of general agents.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"72708078","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":null,"pages":null},"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-Metaverse56740.2022.00284
Shuo Zhang, Chunyang Ye, Hui Zhou
Sentiment analysis for social media can help to explore deeper insight into the attitudes, opinions, and emotions behind the posts. Existing work usually analyze the emoticons and texts of the posts separately, and ignore the impact of emoticons on the emotional polarity of texts. As a result, the polarity of the posts could be marked inaccurately in the scenarios where the polarity of the texts relies on the contextual information of the emoticons. To address this issue, we propose a model, WnhBert-Bi-LSTM, for microblog sentiment analysis. The model trains phrase and emoticon embedding on a large-scale corpus composed of 280,000 Chinese microblogs, and uses the self-attention mechanism to evaluate the impact of emoticons on the overall emotional polarity. By converting emoticons into tractable features, the emoticons can be analyzed jointly with the texts to explore their feature interaction. Evaluations on 8,965 sina microblog posts show that the accuracy of our model is 3.19% higher than the baseline models. In addition, we constructed and open-sourced a new emoticon label corpus with more widely used words and more comprehensive emoticon data than the existing corpus.
{"title":"Sentiment analysis of microblogs with rich emoticons","authors":"Shuo Zhang, Chunyang Ye, Hui Zhou","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00284","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00284","url":null,"abstract":"Sentiment analysis for social media can help to explore deeper insight into the attitudes, opinions, and emotions behind the posts. Existing work usually analyze the emoticons and texts of the posts separately, and ignore the impact of emoticons on the emotional polarity of texts. As a result, the polarity of the posts could be marked inaccurately in the scenarios where the polarity of the texts relies on the contextual information of the emoticons. To address this issue, we propose a model, WnhBert-Bi-LSTM, for microblog sentiment analysis. The model trains phrase and emoticon embedding on a large-scale corpus composed of 280,000 Chinese microblogs, and uses the self-attention mechanism to evaluate the impact of emoticons on the overall emotional polarity. By converting emoticons into tractable features, the emoticons can be analyzed jointly with the texts to explore their feature interaction. Evaluations on 8,965 sina microblog posts show that the accuracy of our model is 3.19% higher than the baseline models. In addition, we constructed and open-sourced a new emoticon label corpus with more widely used words and more comprehensive emoticon data than the existing corpus.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78217988","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-Metaverse56740.2022.00324
Wanning Bao, Liangmin Wang, Jie Chen
The blockchain system requires every node to preserve a complete copy of data arbitrarily, which exerts tremendous storage pressure on nodes. Some researchers applied the erasure code to reduce storage redundancy. However, code storage schemes have the problem of inefficient data communication while verifying transactions and downloading data. To solve this problem, this paper proposes a lightweight locally repairable code (LRC) storage scheme inspired by the idea of slice strategy from privacy computing. Firstly, partitioning each block into distinct transaction slices substantially reduces the amount of transmitted data required to verify a transaction. Secondly, our scheme can recover single-point data with fewer code data slices by local nodes and with less network communication overhead. At last, we analyze the performance of our scheme from theoretical perspectives and examine the storage performance and computation efficiency of our scheme from experimental perspectives. Results suggest that our scheme can effectively reduce the storage overhead while also decreasing the network communication overhead and improving the data reading efficiency.
{"title":"A Lightweight Locally Repairable Code-based Storage Architecture for Blockchains","authors":"Wanning Bao, Liangmin Wang, Jie Chen","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00324","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00324","url":null,"abstract":"The blockchain system requires every node to preserve a complete copy of data arbitrarily, which exerts tremendous storage pressure on nodes. Some researchers applied the erasure code to reduce storage redundancy. However, code storage schemes have the problem of inefficient data communication while verifying transactions and downloading data. To solve this problem, this paper proposes a lightweight locally repairable code (LRC) storage scheme inspired by the idea of slice strategy from privacy computing. Firstly, partitioning each block into distinct transaction slices substantially reduces the amount of transmitted data required to verify a transaction. Secondly, our scheme can recover single-point data with fewer code data slices by local nodes and with less network communication overhead. At last, we analyze the performance of our scheme from theoretical perspectives and examine the storage performance and computation efficiency of our scheme from experimental perspectives. Results suggest that our scheme can effectively reduce the storage overhead while also decreasing the network communication overhead and improving the data reading efficiency.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":null,"pages":null},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77688727","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-Metaverse56740.2022.00170
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":null,"pages":null},"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}
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