Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10151999
Hugo Guercio, Victor Ströele, José Maria N. David, R. Braga
Development communities and the software industry increasingly adopt the Software Ecosystems approach (SECO). This approach can provide advantages but add additional complexity to resource management, affecting the software supply network. Observing SECOs, we can see through three dimensions: business, technical, and social. The social dimension focuses on stakeholders and how they interact with other dimensions. This paper presents a process for analyzing the social dimension of Software Ecosystems, supported by Complex Networks metrics, which allow the presentation of existing SECO relationships’ through visualizations and the use of complex networks. A preliminary evaluation with real data was carried out. The results point to the solution’s viability.
{"title":"A Process to Analyze Software Ecosystem Social Dimension Through a Collaboration Perspective","authors":"Hugo Guercio, Victor Ströele, José Maria N. David, R. Braga","doi":"10.1109/CSCWD57460.2023.10151999","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10151999","url":null,"abstract":"Development communities and the software industry increasingly adopt the Software Ecosystems approach (SECO). This approach can provide advantages but add additional complexity to resource management, affecting the software supply network. Observing SECOs, we can see through three dimensions: business, technical, and social. The social dimension focuses on stakeholders and how they interact with other dimensions. This paper presents a process for analyzing the social dimension of Software Ecosystems, supported by Complex Networks metrics, which allow the presentation of existing SECO relationships’ through visualizations and the use of complex networks. A preliminary evaluation with real data was carried out. The results point to the solution’s viability.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"50 1","pages":"1202-1207"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75813146","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/CSCWD57460.2023.10152760
Jing Tian, Juan Chen, Ningjiang Chen, Lin Bai, Suqun Huang
The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.
{"title":"Knowledge Distillation with Source-free Unsupervised Domain Adaptation for BERT Model Compression","authors":"Jing Tian, Juan Chen, Ningjiang Chen, Lin Bai, Suqun Huang","doi":"10.1109/CSCWD57460.2023.10152760","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152760","url":null,"abstract":"The pre-training language model BERT has brought significant performance improvements to a series of natural language processing tasks, but due to the large scale of the model, it is difficult to be applied in many practical application scenarios. With the continuous development of edge computing, deploying the models on resource-constrained edge devices has become a trend. Considering the distributed edge environment, how to take into account issues such as data distribution differences, labeling costs, and privacy while the model is shrinking is a critical task. The paper proposes a new BERT distillation method with source-free unsupervised domain adaptation. By combining source-free unsupervised domain adaptation and knowledge distillation for optimization and improvement, the performance of the BERT model is improved in the case of cross-domain data. Compared with other methods, our method can improve the average prediction accuracy by up to around 4% through the experimental evaluation of the cross-domain sentiment analysis task.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1766-1771"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75299806","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/CSCWD57460.2023.10152582
Min Feng, Meiju Yu, Ru Li
The Vehicle Named Data Networking(V-NDN) is a vehicular ad-hoc network with the Named Data Networking(NDN) as the architecture, and the most advantageous feature is the in-network cache, which caches the content in the intermediate nodes of the network and can quickly satisfy the requests of subsequent consumers for the same content. Since the cache space of nodes is limited, the cached content should be the popular content frequently requested by users in the network, so the most important problem is accurately finding out the future popular content in the network. This paper designs a multi-feature content popularity prediction algorithm to address this problem based on the attention mechanism and GRU (GRU-Attention). According to the characteristics of multiple historical requests for content, the GRU-Attention model is used to predict the future popularity of content. Through experimental verification, the content popularity prediction algorithm proposed in this paper effectively improves the accuracy of prediction.
{"title":"Multi-feature content popularity prediction algorithm based on GRU-Attention in V-NDN","authors":"Min Feng, Meiju Yu, Ru Li","doi":"10.1109/CSCWD57460.2023.10152582","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152582","url":null,"abstract":"The Vehicle Named Data Networking(V-NDN) is a vehicular ad-hoc network with the Named Data Networking(NDN) as the architecture, and the most advantageous feature is the in-network cache, which caches the content in the intermediate nodes of the network and can quickly satisfy the requests of subsequent consumers for the same content. Since the cache space of nodes is limited, the cached content should be the popular content frequently requested by users in the network, so the most important problem is accurately finding out the future popular content in the network. This paper designs a multi-feature content popularity prediction algorithm to address this problem based on the attention mechanism and GRU (GRU-Attention). According to the characteristics of multiple historical requests for content, the GRU-Attention model is used to predict the future popularity of content. Through experimental verification, the content popularity prediction algorithm proposed in this paper effectively improves the accuracy of prediction.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1136-1141"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89333319","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/CSCWD57460.2023.10152658
Haikun Yu, Dacheng Jiang, Guipeng Zhang, Zhenguo Yang, Wenyin Liu
Existing social networks, such as Twitter and Facebook, are rife with inaccurate and damaging information that is bad for society. Most existing solutions usually use deep learning models for disinformation detection in addition to artificial recognition. However, the result is easily tampered with by people. At the same time, if we strictly manage public opinions, freedom of speech will also cause controversy. In order to solve the above problems and maintain a good social network environment, we propose a new reputation mechanism based on blockchain and deep learning. To assess the reputation of message senders, our proposed mechanism utilizes smart contracts that automate programs without human intervention. Our approach avoids unduly restricting users’ freedom of expression and instead employs deep learning models for rumor detection and sentiment analysis to identify and label messages. By controlling the dissemination of messages based on labels of messages and the sender’s reputation, we aim to balance freedom of speech with social stability. Finally, we analyze the usability and performance of our proposed system.
{"title":"A Reputation System based on Blockchain and Deep Learning in Social Networks","authors":"Haikun Yu, Dacheng Jiang, Guipeng Zhang, Zhenguo Yang, Wenyin Liu","doi":"10.1109/CSCWD57460.2023.10152658","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152658","url":null,"abstract":"Existing social networks, such as Twitter and Facebook, are rife with inaccurate and damaging information that is bad for society. Most existing solutions usually use deep learning models for disinformation detection in addition to artificial recognition. However, the result is easily tampered with by people. At the same time, if we strictly manage public opinions, freedom of speech will also cause controversy. In order to solve the above problems and maintain a good social network environment, we propose a new reputation mechanism based on blockchain and deep learning. To assess the reputation of message senders, our proposed mechanism utilizes smart contracts that automate programs without human intervention. Our approach avoids unduly restricting users’ freedom of expression and instead employs deep learning models for rumor detection and sentiment analysis to identify and label messages. By controlling the dissemination of messages based on labels of messages and the sender’s reputation, we aim to balance freedom of speech with social stability. Finally, we analyze the usability and performance of our proposed system.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"35 1","pages":"630-635"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89339254","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/CSCWD57460.2023.10152787
Wencong Geng, Guijuan Zhang, Dianjie Lu
The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately.
{"title":"An Epidemic Model Based on Intra- and Inter-group Interactions","authors":"Wencong Geng, Guijuan Zhang, Dianjie Lu","doi":"10.1109/CSCWD57460.2023.10152787","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152787","url":null,"abstract":"The global spread of COVID-19 causes great losses to human society. Accurate calculation of the scale of epidemic spread is of great significance for the implementation of corresponding epidemic prevention measures. However, the existing method ignores the group formed by social relations of the population, which reduces the accuracy of the epidemic spread number calculation. In this paper, we propose an epidemic model based on intra- and inter-group interactions. Firstly, we construct a dual network model of epidemic spread based on intra- and inter-group interactions. The network describes how epidemics spread intra- and inter-group. To capture the intergroup influences, we construct a model for social mobility to calculate the inter-group spread rate. Secondly, we propose a computational model for the epidemic spread. We calculate the infection probability of groups in the upper layer network by using a continuous-time Markov chain (CTMC). We describe a dynamic evolution of the intra-group infection in the underlying network based on the mean field equation. And the number of infections in the population is calculated by integrating intra- and inter-group effects. Finally, we implement an epidemic spread simulation system to visualize the spread process. The experimental results show that the model can analyze the epidemic spread process more accurately.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"486-491"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89846841","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/CSCWD57460.2023.10152832
Bin Ning, Fang Liu, Zhixiong Liu
Artificial Intelligence technology-driven Creativity Support Tools (AI-CSTs) provide specific field capability support for human creative activities. In this paper, we compare and analyze the current situation and trend of AI-CSTs design space in four aspects: creative stage, support form, support technology, and role diversity. Through a coding study and comparative analysis of 50 AI-CSTs cases, we discuss the impact of AI-CSTs on traditional workflows, the boundaries of AI-CSTs as co-creators, and how to treat AI errors, which provides insights for future AI-CSTs design. We summarize the collaboration framework in AI-CSTs. Finally, this paper also studies the information technology requirements and challenges of AI-CSTs research, which provides a new perspective to understanding the landscape of AI-CSTs.
{"title":"Creativity Support in AI Co-creative Tools: Current Research, Challenges and Opportunities","authors":"Bin Ning, Fang Liu, Zhixiong Liu","doi":"10.1109/CSCWD57460.2023.10152832","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152832","url":null,"abstract":"Artificial Intelligence technology-driven Creativity Support Tools (AI-CSTs) provide specific field capability support for human creative activities. In this paper, we compare and analyze the current situation and trend of AI-CSTs design space in four aspects: creative stage, support form, support technology, and role diversity. Through a coding study and comparative analysis of 50 AI-CSTs cases, we discuss the impact of AI-CSTs on traditional workflows, the boundaries of AI-CSTs as co-creators, and how to treat AI errors, which provides insights for future AI-CSTs design. We summarize the collaboration framework in AI-CSTs. Finally, this paper also studies the information technology requirements and challenges of AI-CSTs research, which provides a new perspective to understanding the landscape of AI-CSTs.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"82 1","pages":"5-10"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75378485","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/CSCWD57460.2023.10152848
Qin Qin, Chao Tan, Chong Li, G. Ji
Label distribution learning has gained an increasing amount of attention in comparison to single-label and multi-label learning due to its more universal capacity to communicate label ambiguity. Unfortunately, label distribution learning cannot be used directly in many real tasks, because it is very difficult to obtain the label distribution datasets, and many training sets only contain simple logical labels. To resolve this problem and recover the label distributions from the logical labels, label enhancement is proposed. This paper proposes a novel label enhancement algorithm called Privileged Label Enhancement with Adaptive Graph(PLEAG). PLEAG first apply adaptive graph to capture the hidden information between instances and treat it as privileged information. As a result, the similarity matrix of instances is not only influenced by the feature space, but is also adaptively modified in accordance with the degree of similarity between instances in the label space. Then, we adopt RSVM+ model in the paradigm of LUPI (learning with privileged information) to handle the new dataset with privileged information in order to gain better learning effect. Our comparison experiments on 12 datasets show that our proposed algorithm PLEAG , is more accurate than prior label enhancement algorithms for recovering label distribution from logical labels.
与单标签和多标签学习相比，标签分布学习由于具有更普遍的标签歧义交流能力而受到越来越多的关注。不幸的是，标签分布学习不能直接用于许多实际任务，因为很难获得标签分布数据集，而且许多训练集只包含简单的逻辑标签。为了解决这个问题并从逻辑标签中恢复标签分布，提出了标签增强。提出了一种新的标签增强算法——自适应图特权标签增强算法(PLEAG)。PLEAG首先应用自适应图捕获实例间的隐藏信息，并将其作为特权信息处理。这样，实例的相似度矩阵不仅受到特征空间的影响，而且还会根据实例在标签空间中的相似程度自适应地进行修改。然后，为了获得更好的学习效果，我们采用了LUPI (learning with privileged information)范式下的RSVM+模型对新的具有特权信息的数据集进行处理。我们在12个数据集上的对比实验表明，我们提出的PLEAG算法比之前的标签增强算法更准确地从逻辑标签中恢复标签分布。
{"title":"Privileged Label Enhancement with Adaptive Graph","authors":"Qin Qin, Chao Tan, Chong Li, G. Ji","doi":"10.1109/CSCWD57460.2023.10152848","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152848","url":null,"abstract":"Label distribution learning has gained an increasing amount of attention in comparison to single-label and multi-label learning due to its more universal capacity to communicate label ambiguity. Unfortunately, label distribution learning cannot be used directly in many real tasks, because it is very difficult to obtain the label distribution datasets, and many training sets only contain simple logical labels. To resolve this problem and recover the label distributions from the logical labels, label enhancement is proposed. This paper proposes a novel label enhancement algorithm called Privileged Label Enhancement with Adaptive Graph(PLEAG). PLEAG first apply adaptive graph to capture the hidden information between instances and treat it as privileged information. As a result, the similarity matrix of instances is not only influenced by the feature space, but is also adaptively modified in accordance with the degree of similarity between instances in the label space. Then, we adopt RSVM+ model in the paradigm of LUPI (learning with privileged information) to handle the new dataset with privileged information in order to gain better learning effect. Our comparison experiments on 12 datasets show that our proposed algorithm PLEAG , is more accurate than prior label enhancement algorithms for recovering label distribution from logical labels.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"39 4 1","pages":"1867-1872"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75418088","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/CSCWD57460.2023.10152720
Fanshu Gong, Lanju Kong, Yuxuan Lu, Jin Qian, Xinping Min
Blockchain mandates that every node store the whole chain’s history in order to address trust issues in the network. And the storage requirement becomes extremely high, severely affecting the chain’s scalability. To solve such a problem, many optimizations of storage have been proposed. In this paper, existing ways of blockchain storage scalability are described in two categories: off-chain and on-chain. The off-chain way is combined with various distributed and nondistributed storage systems. And on-chain is optimized by changing its block structure, storage rules, or technology. Blockchain technology with scalable storage has been applied in the medical industry. We assess and contrast the methods’ latency, security, and cost. And we point out the problems and challenges of the existing approaches and give an outlook on the future.
{"title":"An Overview of Blockchain Scalability for Storage","authors":"Fanshu Gong, Lanju Kong, Yuxuan Lu, Jin Qian, Xinping Min","doi":"10.1109/CSCWD57460.2023.10152720","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152720","url":null,"abstract":"Blockchain mandates that every node store the whole chain’s history in order to address trust issues in the network. And the storage requirement becomes extremely high, severely affecting the chain’s scalability. To solve such a problem, many optimizations of storage have been proposed. In this paper, existing ways of blockchain storage scalability are described in two categories: off-chain and on-chain. The off-chain way is combined with various distributed and nondistributed storage systems. And on-chain is optimized by changing its block structure, storage rules, or technology. Blockchain technology with scalable storage has been applied in the medical industry. We assess and contrast the methods’ latency, security, and cost. And we point out the problems and challenges of the existing approaches and give an outlook on the future.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"73 1","pages":"516-521"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91234492","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/CSCWD57460.2023.10152556
Xiao-Min Hu, G. Wang, Min Li, Zi-Liang Chen
Traffic signals play an important role in traffic management, and traffic dynamics on the road can be adjusted by changing signal timing. Signal timing optimization and traffic flow prediction are traditionally separate. To improve the effect of signal control, a traffic signal control algorithm for urban intersections based on traffic flow prediction is proposed by combining these two technologies. The goal is to minimize the average delay time of the total vehicles at all signalized intersections in the road network. First, a new Prediction-based Signal Control (PSC) model is proposed, which includes a traffic flow prediction module and a signal timing optimization module. Secondly, a traffic flow prediction strategy and a quantum particle swarm optimization algorithm based on phase angle coding is designed to form the signal control algorithm proposed in this paper. Finally, the PSC algorithm is verified with real traffic data. The results show that the proposed algorithm is better than the fixed signal control and traditional adaptive control algorithms, and the reduction of total queue length and average delay time is significantly improved.
{"title":"A Signal Control Algorithm of Urban Intersections based on Traffic Flow Prediction","authors":"Xiao-Min Hu, G. Wang, Min Li, Zi-Liang Chen","doi":"10.1109/CSCWD57460.2023.10152556","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152556","url":null,"abstract":"Traffic signals play an important role in traffic management, and traffic dynamics on the road can be adjusted by changing signal timing. Signal timing optimization and traffic flow prediction are traditionally separate. To improve the effect of signal control, a traffic signal control algorithm for urban intersections based on traffic flow prediction is proposed by combining these two technologies. The goal is to minimize the average delay time of the total vehicles at all signalized intersections in the road network. First, a new Prediction-based Signal Control (PSC) model is proposed, which includes a traffic flow prediction module and a signal timing optimization module. Secondly, a traffic flow prediction strategy and a quantum particle swarm optimization algorithm based on phase angle coding is designed to form the signal control algorithm proposed in this paper. Finally, the PSC algorithm is verified with real traffic data. The results show that the proposed algorithm is better than the fixed signal control and traditional adaptive control algorithms, and the reduction of total queue length and average delay time is significantly improved.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"72 1","pages":"1372-1377"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91240593","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/CSCWD57460.2023.10152854
Jinbin Tu, Qing Li, Yun Wang
The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic Network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios.
{"title":"T-Sorokin: A General Mobility Model in Opportunistic Networks","authors":"Jinbin Tu, Qing Li, Yun Wang","doi":"10.1109/CSCWD57460.2023.10152854","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152854","url":null,"abstract":"The opportunistic networks are a kind of ad hoc networks that rely on the chance of nodes meeting to transmit messages. Acting as an effective supplement to 4G and 5G networks in some special scenarios where hardware devices are limited, the opportunistic networks have a significant application in health monitoring, warning broadcasting, disaster relief, and so on. The mobility model is one of the research focuses on the opportunistic networks. On the basis of the social mobility theory proposed by Sorokin, a general mobility model, which is suited for various scenarios, called T-Sorokin is proposed. This model is described as a seven-tuple and implemented on the Opportunistic Network Environment simulator and fits both Infocom06 and Rome taxi data set, which includes different areas ranging from hotel to city and different mobile units ranging from person to taxi. The results of experiments demonstrate that the T-Sorokin model has the advantage of generality, simplicity, and accuracy. It can simply establish movement tracks close to real data under different scenarios.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"217 1","pages":"885-890"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74643993","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}
京公网安备 11010802042870号
京ICP备2023020795号-1
扫码关注我们
求助内容：
应助结果提醒方式：