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}
Pub Date : 2022-12-01DOI: 10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00130
Qing Xu, Kaijun Ren, Xiaoli Ren, Shuibing Long, Xiaoyong Li
Knowledge graph embedding (KGE) is an efficient method to predict missing links in knowledge graphs. Most KGE models based on convolutional neural networks have been designed for improving the ability of capturing interaction. Although these models work well, they suffered from the limited receptive field of the convolution kernel, which lead to the lack of ability to capture long-distance interactions. In this paper, we firstly illustrate the interactions between entities and relations and discuss its effect in KGE models by experiments, and then propose MlpE, which is a fully connected network with only three layers. MlpE aims to capture long-distance interactions to improve the performance of link prediction. Extensive experimental evaluations on four typical datasets WN18RR, FB15k-237, DB100k and YAGO3-10 have shown the superority of MlpE, especially in some cases MlpE can achieve the better performance with less parameters than the state-of-the-art convolution-based KGE model.
{"title":"MlpE: Knowledge Graph Embedding with Multilayer Perceptron Networks","authors":"Qing Xu, Kaijun Ren, Xiaoli Ren, Shuibing Long, Xiaoyong Li","doi":"10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00130","DOIUrl":"https://doi.org/10.1109/SmartWorld-UIC-ATC-ScalCom-DigitalTwin-PriComp-Metaverse56740.2022.00130","url":null,"abstract":"Knowledge graph embedding (KGE) is an efficient method to predict missing links in knowledge graphs. Most KGE models based on convolutional neural networks have been designed for improving the ability of capturing interaction. Although these models work well, they suffered from the limited receptive field of the convolution kernel, which lead to the lack of ability to capture long-distance interactions. In this paper, we firstly illustrate the interactions between entities and relations and discuss its effect in KGE models by experiments, and then propose MlpE, which is a fully connected network with only three layers. MlpE aims to capture long-distance interactions to improve the performance of link prediction. Extensive experimental evaluations on four typical datasets WN18RR, FB15k-237, DB100k and YAGO3-10 have shown the superority of MlpE, especially in some cases MlpE can achieve the better performance with less parameters than the state-of-the-art convolution-based KGE model.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"121 10 1","pages":"856-863"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84089415","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":"29 1","pages":"1962-1969"},"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-Metaverse56740.2022.00271
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":"130 1","pages":"1876-1881"},"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}
With the huge amount of crowd mobility data generated by the explosion of mobile devices, deep neural networks (DNNs) are applied to trajectory data mining and modeling, which make great progresses in those scenarios. However, recent studies have demonstrated that DNNs are highly vulnerable to adversarial examples which are crafted by adding subtle, imperceptible noise to normal examples, and leading to the wrong prediction with high confidence. To improve the robustness of modeling spatiotemporal trajectories via DNNs, we propose a collaborative learning model named “Auto-GRU”, which consists of an autoencoder-based self-representation network (SRN) for robust trajectory feature learning and gated recurrent unit (GRU)-based classification network which shares information with SRN for collaborative learning and strictly defending adversarial examples. Our proposed method performs well in defending both white and black box attacks, especially in black-box attacks, where the performance outperforms state-of-the-art methods. Moreover, extensive experiments on Geolife and Beijing taxi traces datasets demonstrate that the proposed model can improve the robustness against adversarial examples without a significant performance penalty on clean examples.
{"title":"Robust Spatio-Temporal Trajectory Modeling Based on Auto-Gated Recurrent Unit","authors":"Jia Jia, Xiaoyong Li, Ximing Li, Linghui Li, Jie Yuan, Hongmiao Wang, Yali Gao, Pengfei Qiu, Jialu Tang","doi":"10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00176","DOIUrl":"https://doi.org/10.1109/smartworld-uic-atc-scalcom-digitaltwin-pricomp-metaverse56740.2022.00176","url":null,"abstract":"With the huge amount of crowd mobility data generated by the explosion of mobile devices, deep neural networks (DNNs) are applied to trajectory data mining and modeling, which make great progresses in those scenarios. However, recent studies have demonstrated that DNNs are highly vulnerable to adversarial examples which are crafted by adding subtle, imperceptible noise to normal examples, and leading to the wrong prediction with high confidence. To improve the robustness of modeling spatiotemporal trajectories via DNNs, we propose a collaborative learning model named “Auto-GRU”, which consists of an autoencoder-based self-representation network (SRN) for robust trajectory feature learning and gated recurrent unit (GRU)-based classification network which shares information with SRN for collaborative learning and strictly defending adversarial examples. Our proposed method performs well in defending both white and black box attacks, especially in black-box attacks, where the performance outperforms state-of-the-art methods. Moreover, extensive experiments on Geolife and Beijing taxi traces datasets demonstrate that the proposed model can improve the robustness against adversarial examples without a significant performance penalty on clean examples.","PeriodicalId":43791,"journal":{"name":"Scalable Computing-Practice and Experience","volume":"16 1","pages":"1189-1194"},"PeriodicalIF":1.1,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82587723","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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