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/CSCWD57460.2023.10152807
Yuzhang Wu, Beilun Wang
Nowadays, with the popularity of the federated learning, it becomes crucial for us to tackle the challenges, communication cost and model robustness. And targeting at the communication bottleneck, data compression is widely used to solve the problem. Besides, the usage of variance reduction for achieving robustness and communication compression for reducing costs has been studied. The Byz-VR-MARINA pro- posed before uses random-sparsification. In this paper, we adopt the absolute compressors hard-threshold and propose a robust compressed framework Byz-VR-BARRY. Experimental results on w8a and a9a datasets have shown the effectiveness of our method, which can decrease the optimality gap obviously.
{"title":"A Framework Using Absolute Compression Hard-Threshold for Improving The Robustness of Federated Learning Model","authors":"Yuzhang Wu, Beilun Wang","doi":"10.1109/CSCWD57460.2023.10152807","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152807","url":null,"abstract":"Nowadays, with the popularity of the federated learning, it becomes crucial for us to tackle the challenges, communication cost and model robustness. And targeting at the communication bottleneck, data compression is widely used to solve the problem. Besides, the usage of variance reduction for achieving robustness and communication compression for reducing costs has been studied. The Byz-VR-MARINA pro- posed before uses random-sparsification. In this paper, we adopt the absolute compressors hard-threshold and propose a robust compressed framework Byz-VR-BARRY. Experimental results on w8a and a9a datasets have shown the effectiveness of our method, which can decrease the optimality gap obviously.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"22 1","pages":"1106-1111"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90192671","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/CSCWD57460.2023.10152735
Jifan Shen, Yuling Sun
With the fast growth of aging population and the spread of various chronic diseases such as heart disease and arthritis among older adults, elderly care has become an urgent topic facing today’s society. Consequently, technologies mediated remote care has become a widely-used method, with the significant promise of reducing cost and improving the efficiency and quality of healthcare. Yet, most remote-caring technologies, especially surveillance video based remote care, face the challenge of privacy issues. For addressing this issue, this paper proposes a privacy- preserved remote care method. Specially, we use ROMP to extract the 3D human model of the elderly in the surveillance video, and use KNN pose estimation algorithm to detect the potential abnormal behaviors. Compared to existing methods, which mainly replace the privacy information with totally different contents, our method not only protects the personal privacy information of the elderly, but also provides clear and identifiable posture information which could better support remote care.
{"title":"Privacy-Preserved Video Monitoring Method with 3D Human Pose Estimation","authors":"Jifan Shen, Yuling Sun","doi":"10.1109/CSCWD57460.2023.10152735","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152735","url":null,"abstract":"With the fast growth of aging population and the spread of various chronic diseases such as heart disease and arthritis among older adults, elderly care has become an urgent topic facing today’s society. Consequently, technologies mediated remote care has become a widely-used method, with the significant promise of reducing cost and improving the efficiency and quality of healthcare. Yet, most remote-caring technologies, especially surveillance video based remote care, face the challenge of privacy issues. For addressing this issue, this paper proposes a privacy- preserved remote care method. Specially, we use ROMP to extract the 3D human model of the elderly in the surveillance video, and use KNN pose estimation algorithm to detect the potential abnormal behaviors. Compared to existing methods, which mainly replace the privacy information with totally different contents, our method not only protects the personal privacy information of the elderly, but also provides clear and identifiable posture information which could better support remote care.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"98 1","pages":"1502-1507"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90307105","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}
Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152684
Aimei Dong, Zhiyun Qi, Yi Zhai, Guohua Lv
Epilepsy is a neurological disease that occurs in all ages and seriously threatens physical and mental health. There are two problems in the present study. One is the limitation of the amount of publicly available medical data. And the other is that the distributions of the data are different but correlated. Conventional machine learning methods are not applicable. But transfer learning method has shown promising performance in solving both problems. In this paper, a multi-source domain transfer learning method called MDTL for epilepsy diagnosis is proposed. In order to fully exploit the specific features and common features of the dataset, we propose a domain specific feature extractor and a common feature extractor. For enhancing data, we transform the signals into time-frequency diagrams to rotate and crop. The three types of electrocardiogram (ECG) time-frequency diagram are put to train model, and the model is transferred to electroencephalogram (EEG) time-frequency diagrams. The results confirm that MDTL is effective in epilepsy diagnosis.
{"title":"Multi-Source Domain Transfer Learning on Epilepsy Diagnosis","authors":"Aimei Dong, Zhiyun Qi, Yi Zhai, Guohua Lv","doi":"10.1109/CSCWD57460.2023.10152684","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152684","url":null,"abstract":"Epilepsy is a neurological disease that occurs in all ages and seriously threatens physical and mental health. There are two problems in the present study. One is the limitation of the amount of publicly available medical data. And the other is that the distributions of the data are different but correlated. Conventional machine learning methods are not applicable. But transfer learning method has shown promising performance in solving both problems. In this paper, a multi-source domain transfer learning method called MDTL for epilepsy diagnosis is proposed. In order to fully exploit the specific features and common features of the dataset, we propose a domain specific feature extractor and a common feature extractor. For enhancing data, we transform the signals into time-frequency diagrams to rotate and crop. The three types of electrocardiogram (ECG) time-frequency diagram are put to train model, and the model is transferred to electroencephalogram (EEG) time-frequency diagrams. The results confirm that MDTL is effective in epilepsy diagnosis.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"11 1","pages":"83-88"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74834007","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/CSCWD57460.2023.10152725
Beibei Cheng, Yiming Zhu, Yuxuan Chen, Xiaodan Gu, Kai Dong
The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.
{"title":"Privacy Protection Based on Packet Filtering for Home Internet-of-Things","authors":"Beibei Cheng, Yiming Zhu, Yuxuan Chen, Xiaodan Gu, Kai Dong","doi":"10.1109/CSCWD57460.2023.10152725","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152725","url":null,"abstract":"The development of home internet of things (H-IoT) devices brings convenience but poses significant privacy and security risks. Existing research minimizes data uploaded to the cloud but fails to process data locally, resulting in a trade-off between privacy and functionality. In this paper, we propose a privacy-preserving method that identifies and processes sensitive data sent from H-IoT devices at the edge side, ensuring functionality while preserving privacy. Our method applies different identification strategies to packets with different features, making it applicable to most H-IoT devices and scenarios. We validate our approach through experiments on a prototype system that monitors multiple cameras, demonstrating its effectiveness in preserving privacy while maintaining functionality.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"16 1","pages":"1214-1219"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75553588","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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