Haze can reduce the visibility of the captured image, making it hard to accurately distinguish the details of each object in the captured image scene. Aiming at the problem of detail loss in existing dehazing models, this paper proposes a lightweight end-to-end image dehazing framework called DFE-GAN (Detail Feature Enhancement-GAN). The missing detail contours in the haze image can be predicted by employing a densely connected detail feature prediction network. Supplemented with a patch discriminator and an improved loss function, the restoration of details in the dehazing image is enhanced to improve image quality. We apply inverse residual modules to extract and fuse multi-scale features from images, which can ensure the real-time processing capability of the model. Compared with previous state-of-the-art approaches, solid experimental results on various benchmark datasets validate the robustness and effectiveness of our model.
{"title":"Lightweight Image Dehazing Algorithm Based on Detail Feature Enhancement","authors":"Chenxing Gao, Lingjun Chen, Caidan Zhao, Xiangyu Huang, Zhiqiang Wu","doi":"10.1109/CSCWD57460.2023.10152843","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152843","url":null,"abstract":"Haze can reduce the visibility of the captured image, making it hard to accurately distinguish the details of each object in the captured image scene. Aiming at the problem of detail loss in existing dehazing models, this paper proposes a lightweight end-to-end image dehazing framework called DFE-GAN (Detail Feature Enhancement-GAN). The missing detail contours in the haze image can be predicted by employing a densely connected detail feature prediction network. Supplemented with a patch discriminator and an improved loss function, the restoration of details in the dehazing image is enhanced to improve image quality. We apply inverse residual modules to extract and fuse multi-scale features from images, which can ensure the real-time processing capability of the model. Compared with previous state-of-the-art approaches, solid experimental results on various benchmark datasets validate the robustness and effectiveness of our model.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"4 1","pages":"1538-1543"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80404609","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152705
Wenxu Han, Qi Li, Meiju Yu, Ru Li
With the explosive development of technology and Internet communication, it has become an inevitable trend to realize the secure storage and sharing of electronic medical data among hospitals. In recent researches, there are also many problems in realizing secure storage and sharing of electronic medical data, such as "data silos", leakage of patient sensitive information due to data sharing and having no reliability about the original data uploaded by patients. To solve the above problems, we propose a blockchain-based medical data storage and secure sharing scheme. In the scheme, we utilize IPFS-based Web3.Storage for medical data storage, propose a sensitivity classification and access control strategy for sensitive data leakage and present a blockchain-based original data reliability checking strategy to check the reliability of the original data. Our scheme is explained in detail in the paper, and the performance analysis of this scheme is carried out to prove the feasibility of this scheme.
{"title":"Research on Medical Data Storage and Secure Sharing Scheme Based on Blockchain","authors":"Wenxu Han, Qi Li, Meiju Yu, Ru Li","doi":"10.1109/CSCWD57460.2023.10152705","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152705","url":null,"abstract":"With the explosive development of technology and Internet communication, it has become an inevitable trend to realize the secure storage and sharing of electronic medical data among hospitals. In recent researches, there are also many problems in realizing secure storage and sharing of electronic medical data, such as \"data silos\", leakage of patient sensitive information due to data sharing and having no reliability about the original data uploaded by patients. To solve the above problems, we propose a blockchain-based medical data storage and secure sharing scheme. In the scheme, we utilize IPFS-based Web3.Storage for medical data storage, propose a sensitivity classification and access control strategy for sensitive data leakage and present a blockchain-based original data reliability checking strategy to check the reliability of the original data. Our scheme is explained in detail in the paper, and the performance analysis of this scheme is carried out to prove the feasibility of this scheme.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"07 1","pages":"879-884"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79378093","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152633
Z. Liu, Meiju Yu, Ru Li
The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.
{"title":"Blockchain-based Trust Management Mechanism in V-NDN","authors":"Z. Liu, Meiju Yu, Ru Li","doi":"10.1109/CSCWD57460.2023.10152633","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152633","url":null,"abstract":"The Vehicular Named Data Networking (V-NDN) improves the speed of message acquisition between vehicles and reduces network overhead by using a in-network caching mechanism. The vehicles in V-NDN have the capability of built-in caching, in other words, they can cache contents passing by and provide content services for users. However, malicious nodes in V-NDN might apply fake messages for malicious purposes, which is one of the major risks of network security. In this paper, we build a trust management mechanism based on blockchain to solve the above problems. In the proposed mechanism, vehicles first judge the credibility of the received message based on the vehicle reputation value and the feature of the message itself. Then the vehicle reputation value is updated according to the message credibility. Finally, the blockchain is used to realize the consensus of the message credibility and the vehicle reputation value. We conduct experiments on the simulation platform and simulation results show that the proposed mechanism can effectively improve the accuracy of message credibility judgment and malicious vehicles detection, thereby improving the security of the V-NDN.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"4 1","pages":"1433-1438"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76341440","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152841
Jiahui Liu, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang
Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.
{"title":"An Improved MOEA Based on Adaptive Adjustment Strategy for Optimizing Deep Model of RFID Indoor Positioning","authors":"Jiahui Liu, Lvqing Yang, Sien Chen, Wensheng Dong, Bo Yu, Qingkai Wang","doi":"10.1109/CSCWD57460.2023.10152841","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152841","url":null,"abstract":"Nowadays, IoT technology is developing rapidly and RFID (Radio Frequency Identification) based indoor positioning problems can be performed using deep learning and intelligent optimization algorithms. Deep models can analyze and predict the localization problem as a regression problem to achieve high accuracy positioning. Meanwhile, to ensure the accuracy of the model, we need to find excellent hyperparameters, which requires the support of optimization algorithms, but existing optimization algorithms do not allow flexible adaptation according to the optimization phase and there is room for improvement. In this paper, we propose a deep model, called CTT, and a multi-objective evolutionary algorithm (MOEA) based on a neighborhood adaptive adjustment strategy, called MOEA-NAAS. The experimental results show that CTT optimized by the NAAS algorithm is significantly more accurate and stable in the localization problem, with significant improvements in the three main metrics, proving the usability of the optimization algorithm. At the same time, the localization effect of the CTT also shows obvious advantages. In the future, the optimized algorithm can be combined with other deep models and widely used in various high-precision indoor positioning.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"36 1","pages":"357-362"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"76837658","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152711
Chaokai Wu, Yansong Wang, Tao Jia
The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future time. How to effectively learn the temporal and structural pattern of the network dynamics is the key. In this paper, we propose a graph representation learning model based on enhanced structure and temporal information (GRL_EnSAT). For structural information, we exploit a combination of a graph attention network (GAT) and a self-attention network to capture structural neighborhood. For temporal dynamics, we use a masked self-attention network to capture the dynamics in the link evolution. In this way, GRL_EnSAT not only learns low-dimensional embedding vectors but also preserves the nonlinear dynamic feature of the evolving network. GRL_EnSAT is evaluated on four real datasets, in which GRL_EnSAT outperforms most advanced baselines. Benefiting from the dynamic self-attention mechanism, GRL_EnSAT yields better performance than approaches based on recursive graph evolution modeling.
{"title":"Dynamic Link Prediction Using Graph Representation Learning with Enhanced Structure and Temporal Information","authors":"Chaokai Wu, Yansong Wang, Tao Jia","doi":"10.1109/CSCWD57460.2023.10152711","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152711","url":null,"abstract":"The links in many real networks are evolving with time. The task of dynamic link prediction is to use past connection histories to infer links of the network at a future time. How to effectively learn the temporal and structural pattern of the network dynamics is the key. In this paper, we propose a graph representation learning model based on enhanced structure and temporal information (GRL_EnSAT). For structural information, we exploit a combination of a graph attention network (GAT) and a self-attention network to capture structural neighborhood. For temporal dynamics, we use a masked self-attention network to capture the dynamics in the link evolution. In this way, GRL_EnSAT not only learns low-dimensional embedding vectors but also preserves the nonlinear dynamic feature of the evolving network. GRL_EnSAT is evaluated on four real datasets, in which GRL_EnSAT outperforms most advanced baselines. Benefiting from the dynamic self-attention mechanism, GRL_EnSAT yields better performance than approaches based on recursive graph evolution modeling.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"5 1","pages":"279-284"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87066427","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152692
Zuyang Ma, Kaihong Yan, Hongwei Wang
In order to improve the resource utilization rate of existing nuclear power data and promote workers to efficiently obtain the operation information of nuclear power units and assist them in fault diagnosis and maintenance decision-making, this paper constructs a knowledge graph question answering (KGQA) dataset in the field of nuclear power. The BEm-KGQA model based on the pre-trained language model and knowledge graph embedding method was proposed. Our model learns the embedded representation of the knowledge graph through BERT and fine-tunes the BERT model. In the question embedding stage, it learns the embedded representation of the question based on the fine-tuned BERT model. Through experiments, we demonstrate the effectiveness of the method over other models. In addition, this paper implements a nuclear power question answering system. Based on the question answering system, employees can learn about unit information and efficiently obtain information on unusual operating events of nuclear power.
{"title":"BERT-based Question Answering using Knowledge Graph Embeddings in Nuclear Power Domain","authors":"Zuyang Ma, Kaihong Yan, Hongwei Wang","doi":"10.1109/CSCWD57460.2023.10152692","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152692","url":null,"abstract":"In order to improve the resource utilization rate of existing nuclear power data and promote workers to efficiently obtain the operation information of nuclear power units and assist them in fault diagnosis and maintenance decision-making, this paper constructs a knowledge graph question answering (KGQA) dataset in the field of nuclear power. The BEm-KGQA model based on the pre-trained language model and knowledge graph embedding method was proposed. Our model learns the embedded representation of the knowledge graph through BERT and fine-tunes the BERT model. In the question embedding stage, it learns the embedded representation of the question based on the fine-tuned BERT model. Through experiments, we demonstrate the effectiveness of the method over other models. In addition, this paper implements a nuclear power question answering system. Based on the question answering system, employees can learn about unit information and efficiently obtain information on unusual operating events of nuclear power.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"111 1","pages":"267-272"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86239986","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152820
L. Silva, Marcos Calazans, L. Vasconcelos, Raissa Barcellos, D. Trevisan, J. Viterbo
Urban population growth creates problems such as congestion and resource scarcity. These problems contribute to poor quality of life and negative environmental impacts. In this context, Information and Communication Technologies appear to improve sustainability solutions. Smart Mobility emerges as a dimension of the Smart City and includes technologies and applications that assist transport services. Among these services, the applications directed to the cyclist segment stand out. In our work, we present a review of bicycle applications, and we perform a comparative function analysis and their relationship with the factors that contribute to the practice of cycling filtering the most relevant functions. We aim to find the most attractive features for urban cyclists and the limitations of what is offered in the market. In addition, we will provide guidance to improve the development of cycling apps and the implementation of new features, collaborating with the development of new technologies and future research.
{"title":"Smart Cities in Focus: A Bicycle Transport Applications Analysis","authors":"L. Silva, Marcos Calazans, L. Vasconcelos, Raissa Barcellos, D. Trevisan, J. Viterbo","doi":"10.1109/CSCWD57460.2023.10152820","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152820","url":null,"abstract":"Urban population growth creates problems such as congestion and resource scarcity. These problems contribute to poor quality of life and negative environmental impacts. In this context, Information and Communication Technologies appear to improve sustainability solutions. Smart Mobility emerges as a dimension of the Smart City and includes technologies and applications that assist transport services. Among these services, the applications directed to the cyclist segment stand out. In our work, we present a review of bicycle applications, and we perform a comparative function analysis and their relationship with the factors that contribute to the practice of cycling filtering the most relevant functions. We aim to find the most attractive features for urban cyclists and the limitations of what is offered in the market. In addition, we will provide guidance to improve the development of cycling apps and the implementation of new features, collaborating with the development of new technologies and future research.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"15 1","pages":"855-860"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80674281","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152593
Cheng Chen, Jie Zhu, Haiping Huang, Yingmeng Gao
The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.
{"title":"Energy-Constrained Task Scheduling in Heterogeneous Distributed Systems","authors":"Cheng Chen, Jie Zhu, Haiping Huang, Yingmeng Gao","doi":"10.1109/CSCWD57460.2023.10152593","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152593","url":null,"abstract":"The resource-constrained task scheduling problem has been one of the popular research topics in cloud computing systems. By employing the dynamic voltage and frequency scaling (DVFS) techniques, the task scheduling can be further constrained by energy consumption. The paper investigates the DAG task scheduling considering both the resource and energy constraints in heterogeneous distributed systems. The objective is to minimize the scheduling length. An energy-constrained task scheduling framework is employed, where tasks are initially scheduled according to their upward rank values. Then two heuristics are proposed to improve the initial solution, namely, the simulated annealing local search method and the frequency adjustment method. Experiments are conducted by testing a large number of instances with multiple parameter settings, and the results show that the proposed algorithms are effective and efficient.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"101 1","pages":"1902-1907"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80841828","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152569
Xiaojiao Xie, Pengwei Zhan
Natural language models have been widely used for their impressive performance in various tasks, while their poor robustness also puts critical applications at high risk. These models are vulnerable to adversarial examples, which contain imperceptible noise that leads the model to wrong predictions. To ensure such malicious examples are imperceptible to humans, various word-level attack methods have been proposed. Previous works on word-level attacks attempt to generate adversarial examples by substituting words in sentences. They utilize different candidate substitution selection methods and substitution strategies to improve attack effectiveness and the quality of generated examples. However, previous works are all unigram-based attack methods, which ignore the connection between words. The unigram nature of these methods downgrades fluency, increases grammatical errors, and biases the semantics of adversarial examples, making adversarial examples easier to be detected by humans. In this paper, to improve the quality of textual adversarial examples and makes the adversarial example more imperceptible to human, we propose a black-box word-level attack method called Dynamic N-Gram Based Attack (DyGram). DyGram tokenizes the entire sentence into multiple n-gram units, rather than individual words as in previous works, and substitutes words in a sentence in descending order of n-gram unit importance. Extensive experiments demonstrate that DyGram achieves higher attack success rates than previous attack methods and improves the quality of generated adversarial examples in terms of the number of perturbed words, perplexity, grammatical correctness, and semantic similarity.
{"title":"Improving the Quality of Textual Adversarial Examples with Dynamic N-gram Based Attack","authors":"Xiaojiao Xie, Pengwei Zhan","doi":"10.1109/CSCWD57460.2023.10152569","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152569","url":null,"abstract":"Natural language models have been widely used for their impressive performance in various tasks, while their poor robustness also puts critical applications at high risk. These models are vulnerable to adversarial examples, which contain imperceptible noise that leads the model to wrong predictions. To ensure such malicious examples are imperceptible to humans, various word-level attack methods have been proposed. Previous works on word-level attacks attempt to generate adversarial examples by substituting words in sentences. They utilize different candidate substitution selection methods and substitution strategies to improve attack effectiveness and the quality of generated examples. However, previous works are all unigram-based attack methods, which ignore the connection between words. The unigram nature of these methods downgrades fluency, increases grammatical errors, and biases the semantics of adversarial examples, making adversarial examples easier to be detected by humans. In this paper, to improve the quality of textual adversarial examples and makes the adversarial example more imperceptible to human, we propose a black-box word-level attack method called Dynamic N-Gram Based Attack (DyGram). DyGram tokenizes the entire sentence into multiple n-gram units, rather than individual words as in previous works, and substitutes words in a sentence in descending order of n-gram unit importance. Extensive experiments demonstrate that DyGram achieves higher attack success rates than previous attack methods and improves the quality of generated adversarial examples in terms of the number of perturbed words, perplexity, grammatical correctness, and semantic similarity.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"53 1","pages":"594-599"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81156230","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152793
Bo Li, Zisu Na, Rongrong Qian, Hongwei Ding
How to ensure the reliability of mixed communication between vehicle to vehicle and vehicle to road, and how to ensure seamless handover during communication in the Internet of Vehicles, is a research hotspot in 5G cellular Internet of Vehicles. This paper proposes a 5G cellular Internet of Vehicles communication handover strategy model for V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) scenarios, which combines fuzzy logic and user satisfaction for obtaining quality of service, and can make intelligent decisions on handover trigger timing and handover targets based on various parameters in the wireless network, so that the vehicle can handover to the best service target in the process of vehicle terminal handover. The experimental study shows that the proposed method outperforms other common methods in terms of handover metrics such as handover trigger rate, handover times and quality of service compared to existing studies.
如何保证车与车、车与路混合通信的可靠性，保证车联网通信过程中的无缝切换，是5G蜂窝车联网的研究热点。针对V2V (Vehicle to Vehicle)和V2I (Vehicle to Infrastructure)场景，提出了一种5G蜂窝车联网通信切换策略模型，该模型将模糊逻辑和用户满意度相结合以获取服务质量，根据无线网络中的各种参数对切换触发时间和切换目标进行智能决策，使车辆在车载终端切换过程中能够切换到最佳服务目标。实验研究表明，该方法在切换触发率、切换次数和服务质量等切换指标上均优于其他常用方法。
{"title":"QoS and Fuzzy Logic Based Communication Handover Strategy in 5G Cellular Internet of Vehicles","authors":"Bo Li, Zisu Na, Rongrong Qian, Hongwei Ding","doi":"10.1109/CSCWD57460.2023.10152793","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152793","url":null,"abstract":"How to ensure the reliability of mixed communication between vehicle to vehicle and vehicle to road, and how to ensure seamless handover during communication in the Internet of Vehicles, is a research hotspot in 5G cellular Internet of Vehicles. This paper proposes a 5G cellular Internet of Vehicles communication handover strategy model for V2V (Vehicle to Vehicle) and V2I (Vehicle to Infrastructure) scenarios, which combines fuzzy logic and user satisfaction for obtaining quality of service, and can make intelligent decisions on handover trigger timing and handover targets based on various parameters in the wireless network, so that the vehicle can handover to the best service target in the process of vehicle terminal handover. The experimental study shows that the proposed method outperforms other common methods in terms of handover metrics such as handover trigger rate, handover times and quality of service compared to existing studies.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1458-1463"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85417101","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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