Pub Date : 2023-05-24DOI: 10.1109/CSCWD57460.2023.10152830
Ji Zheng, D. Ding, Yuang Zhang, Zidu Cheng, Zhuying Li
Depression is a severe mental illness that can lead to negative moods and activities. The traditional clinical approach for diagnosing depression is face-to-face consultation, which is limited by time and space. Virtual Reality (VR), as a novel technology with higher accessibility and lower cost, can serve as an effective digital approach to diagnosing psychological disorders. In VR systems, users are exposed to various experimental scenarios, gaining immersive and interactive experiences. Recent research has demonstrated a relationship between depression and low spatial memory navigation ability (SMNA). Based on these considerations, we propose a VR system to detect one’s depression level by measuring spatial memory navigation performances. The system consists of three virtual scenarios with different spatial scales and dimensions. To study the system’s effectiveness, a pilot study with eight participants was conducted. The results showed differences in the participants’ spatial memory navigation performances in the three scenarios and a correlation between depression level and their spatial memory navigation performances.
{"title":"VRNavigSS: A Two-dimensionality Virtual Reality System for Depression Level Detection","authors":"Ji Zheng, D. Ding, Yuang Zhang, Zidu Cheng, Zhuying Li","doi":"10.1109/CSCWD57460.2023.10152830","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152830","url":null,"abstract":"Depression is a severe mental illness that can lead to negative moods and activities. The traditional clinical approach for diagnosing depression is face-to-face consultation, which is limited by time and space. Virtual Reality (VR), as a novel technology with higher accessibility and lower cost, can serve as an effective digital approach to diagnosing psychological disorders. In VR systems, users are exposed to various experimental scenarios, gaining immersive and interactive experiences. Recent research has demonstrated a relationship between depression and low spatial memory navigation ability (SMNA). Based on these considerations, we propose a VR system to detect one’s depression level by measuring spatial memory navigation performances. The system consists of three virtual scenarios with different spatial scales and dimensions. To study the system’s effectiveness, a pilot study with eight participants was conducted. The results showed differences in the participants’ spatial memory navigation performances in the three scenarios and a correlation between depression level and their spatial memory navigation performances.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"820-824"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90816895","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/CSCWD57460.2023.10152589
Jia-xu Fan, Chunjiang Zhang, Weiming Shen
This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.
{"title":"A Matheuristic-based Rescheduling Method for Flexible Job Shops with Lot-streaming and Machine Reconfigurations","authors":"Jia-xu Fan, Chunjiang Zhang, Weiming Shen","doi":"10.1109/CSCWD57460.2023.10152589","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152589","url":null,"abstract":"This paper studies a flexible job shop rescheduling problem with lot-streaming and machine reconfigurations (FJRP-LSMR) to minimize the sum of the instability and total weighted tardiness, where machine reconfigurations are performed by assembling selected auxiliary modules for processing different batches of products. In this case, a rescheduling process is triggered by dynamic events, and requires to determine the lot-sizing plan, machine assignment, and sublot sequencing simultaneously. To address the intractable problem with multiple decision-making processes, a matheuristic integrating the genetic algorithm (GA) and the mixed integer linear programming (MILP) technique is proposed, where an MILP model is developed for optimally solving the lot-sizing sub-problem, and is embedded to the GA as a local search function. The proposed matheuristic is tested on randomly-generated instances to investigate the performance of all the algorithmic components. Experimental results demonstrate that the GA representation is effective in the complicated dynamic scheduling problem, and the lot-sizing sub-problem can be well addressed by the proposed MILP-based local search.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"1 1","pages":"1950-1955"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89643562","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}
This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.
{"title":"A Variable Neighborhood Search Algorithm for Heat Pipe-Constrained Component Layout Optimization","authors":"Shichen Tian, Zhi-Guo Deng, Jia-xu Fan, Chunjiang Zhang, Weiming Shen, Liang Gao","doi":"10.1109/CSCWD57460.2023.10152572","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152572","url":null,"abstract":"This paper proposes a bi-level Multi-Start Variable Neighborhood Search-Genetic Algorithm (MSVNS-GA) for the heat pipe-constrained component layout optimization (HCLO) problems. The proposed algorithm has won the first place in the CEC’2022 Competition on the Heat Pipe-Constrained Component Layout Optimization. First, the HCLO problem is divided into two sub-problems, heat pipe assignment (HA) and component location (CL). In the HA problem, components are assigned to different heat pipes. The best assignment scheme is taken as the input of the CL problem. In the CL problem, the specific coordinates of components are determined to meet practical engineering constraints. In this way, the complexity of the problem is lowered, and a part of the infeasible solution is cropped. Second, to address the HA problem, a multi-start variable neighborhood search algorithm is proposed and five efficient bottleneck-aware neighborhood structures are designed. And the genetic algorithm is used for CL problem. Finally, 30 independent experiments are carried out on the calculation examples with sizes of 6×4, 15×6, 40×16, and 90×32. The best result obtained by MSVNS-GA is 0.0%, 1.0%, 0.8%, and 1.1% different from the estimated lower bounds.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"13 1","pages":"1452-1457"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89521941","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}
With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.
{"title":"Joint Optimization of Task Offloading and Resource Allocation for Edge Video Analytics","authors":"Zhenxuan Xu, Yunzhou Xie, Fang Dong, Shucun Fu, Jiangshan Hao","doi":"10.1109/CSCWD57460.2023.10152681","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152681","url":null,"abstract":"With the development of artificial intelligence technology and intelligent devices, people show great interest in intelligent applications and services, but it is impossible to complete these compute-intensive AI tasks locally, especially video analysis tasks. Edge computing is regarded as an appropriate solution to these problems. In this paper, we study the multi-user multi-server edge-end collaboration video analytics task offloading problem aiming at minimizing the overall delay for each device to finish its task. Each device chooses whether to execute the task locally or to offload the task to an edge server, and which edge server to select. At the theoretical level, we model the joint problem of task offloading and resource allocation as a mixed integer programming problem. We first determine the optimal resource allocation policy with a given task offloading decision profile. Then, task offloading problem is modeled as a congestion game and propose a decentralized mechanism to achieve a Nash equilibrium. Moreover, experimental results demonstrate that the proposed method is efficient and can significantly and steadily improve the system performance, reducing the overall delay by 33.96% on average, compared with other algorithms.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"83 1","pages":"636-641"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"91234783","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/CSCWD57460.2023.10152719
Jia Peng, Neng Gao, Yifei Zhang, Min Li
The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and deductible. Semantic indiscriminate knowledge representation models usually focus more on the scalability on real world knowledge graphs. They assume that the vector representations of entities and relations are consistent in any semantic environment. Semantic discriminate knowledge representation models focus more on precision. They assume that the vector representations should depend on the specific semantic environment. However, both the two kinds only consider knowledge embedding in semantic space, ignoring the rich features of network structure contained between triplet entities. The MulSS model proposed in this paper is a joint embedding learning method across network structure space and semantic space. By synchronizing the Deepwalk network representation learning method into the semantic indiscriminate model TransE, MulSS achieves better performance than TransE and some semantic discriminate knowledge representation models on triplet classification task. This shows that it is of great significance to extend knowledge representation learning from the single semantic space to the network structure and semantic joint space.
{"title":"A Multi-view Knowledge Graph Embedding Model Considering Structure and Semantics","authors":"Jia Peng, Neng Gao, Yifei Zhang, Min Li","doi":"10.1109/CSCWD57460.2023.10152719","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152719","url":null,"abstract":"The essence of knowledge representation learning is to embed the knowledge graph into a low-dimensional vector space to make knowledge computable and deductible. Semantic indiscriminate knowledge representation models usually focus more on the scalability on real world knowledge graphs. They assume that the vector representations of entities and relations are consistent in any semantic environment. Semantic discriminate knowledge representation models focus more on precision. They assume that the vector representations should depend on the specific semantic environment. However, both the two kinds only consider knowledge embedding in semantic space, ignoring the rich features of network structure contained between triplet entities. The MulSS model proposed in this paper is a joint embedding learning method across network structure space and semantic space. By synchronizing the Deepwalk network representation learning method into the semantic indiscriminate model TransE, MulSS achieves better performance than TransE and some semantic discriminate knowledge representation models on triplet classification task. This shows that it is of great significance to extend knowledge representation learning from the single semantic space to the network structure and semantic joint space.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"29 1","pages":"1532-1537"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"83511477","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}
Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.
{"title":"A Multi-level Approach to Learning Early Warning based on Cognitive Diagnosis and Learning Behaviors Analysis","authors":"Hua Ma, Wen Zhao, Zixu Jiang, Peiji Huang, Wen-sheng Tang, Hongyu Zhang","doi":"10.1109/cscwd57460.2023.10152579","DOIUrl":"https://doi.org/10.1109/cscwd57460.2023.10152579","url":null,"abstract":"Learning early warning is of great significance for coping with students' learning risks. The existing research fails in modeling the fluctuation of students' learning states and providing the multi-level early warning for students at different levels. To address them, a new approach of learning early warning is proposed to predict at-risk students in e-learning environment by combining cognitive diagnosis with learning behaviors analysis. In this approach, the students' learning process is modeled from four dimensions, i.e., learning quality, learning engagement, latent learning state, and historical learning performance. The convolutional neural network and long short-term memory network are used to explore the students' latent learning features. Then, the Adaboost algorithm is applied to predict students' learning performance. Based on the predicted performance, the evaluation rules are designed to provide multi-level learning early warning for students. Finally, the experiments demonstrate that the proposed method could predict at-risk students efficiently and accurately.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"79 1","pages":"468-473"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"82053243","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/CSCWD57460.2023.10152617
Y. Lima, C. E. Barbosa, A. Lyra, Herbert Salazar, M. Argôlo, J. Souza
Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.
{"title":"Providing Patients with Actionable Medical Knowledge: mHealth Apps for Laypeople","authors":"Y. Lima, C. E. Barbosa, A. Lyra, Herbert Salazar, M. Argôlo, J. Souza","doi":"10.1109/CSCWD57460.2023.10152617","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152617","url":null,"abstract":"Healthcare practitioners are professionals with highly specialized knowledge leaving a vast gap between them and their patients. Mobile Health applications may provide a fast and precise diagnosis to patients through expert systems and chatbots. We surveyed and classified Mobile Health apps, discussing their advantages, such as lower costs and replicability. However, most technologies lack the common sense and creativity to solve individual cases, and their precision is far from that of humans. Mobile Health is a relatively new field, and new technologies will be developed in the future, changing the current balance in favor of machines but not replacing healthcare professionals completely. This trend should be watched closely by those interested in healthcare, given its potential for the improvement of patient treatment and also their capacity to disrupt healthcare professionals’ formation and work. Therefore, this work contributes to understanding the capabilities and limitations of mHealth apps in providing medical diagnosis and treatment.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"35 1","pages":"654-659"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77235061","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}
With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user’s cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms.
{"title":"Cost-Optimized Microservice Deployment for IoT Application in Cloud-Edge Collaborative Environment","authors":"Xiaoyuan Zhang, Bing Tang, Qing Yang, Wei Xu, Feiyan Guo","doi":"10.1109/CSCWD57460.2023.10152549","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152549","url":null,"abstract":"With the popularity of cloud native and DevOps, container technology is widely used and combined with microservices. The deployment of container-based microservices in distributed cloud-edge infrastructure requires suitable strategies to ensure the quality of service for users. However, the existing container orchestration tools cannot flexibly select the best deployment location according to the user’s cost budget, and are insufficient in personalized deployment solutions. From the perspective of application providers, this paper considers the location distribution of users, application dependencies, and server price differences, and proposes a genetic algorithm-based Internet-of-Things (IoT) application deployment strategy for personalized cost budgets. The application deployment problem is defined as an optimization problem that minimizes user service latency under cost constraints. This problem is an NP-hard problem, and genetic algorithm is introduced to solve the optimization problem effectively and improve the deployment efficiency. The proposed algorithm is compared with four baseline algorithms, Time-Greedy, Cost-Greedy, Random and PSO, using real datasets and some synthetic datasets. The results show that the proposed algorithm outperforms other competing baseline algorithms.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"64 1","pages":"873-878"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85059063","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/CSCWD57460.2023.10152729
Chundong Wang, Yue Li
Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.
{"title":"A Copyright Authentication Method Balancing Watermark Robustness and Data Distortion","authors":"Chundong Wang, Yue Li","doi":"10.1109/CSCWD57460.2023.10152729","DOIUrl":"https://doi.org/10.1109/CSCWD57460.2023.10152729","url":null,"abstract":"Database watermarking plays an irreplaceable role in copyright authentication and data integrity protection, but the robustness of the watermark and the resulting data distortion are a pair of contradictory objects that cannot be ignored. To solve this problem, a reversible database watermarking method, named IGADEW, is proposed to balance the relationship between them. The biggest difference from previous research is that IGADEW synthesizes the optimization objects and obtain various parameters through genetic algorithm (GA). Second, the fitness function considers the weights of robustness and distortion, aiming to find the optimal balance between the two. IGADEW uses the Hash-based Message Authentication Code (HMAC) algorithm to encrypt the experimental parameters and uses the primary key hash algorithm for data grouping, both to ensure robustness. And the data distortion is limited with the help of threshold constraints. Finally, experiments using the UCI dataset demonstrate the effectiveness of IGADEW. Experimental results show that, compared with existing methods, IGADEW is more robust against common attacks, with lower data distortion.","PeriodicalId":51008,"journal":{"name":"Computer Supported Cooperative Work-The Journal of Collaborative Computing","volume":"44 1","pages":"1178-1183"},"PeriodicalIF":2.4,"publicationDate":"2023-05-24","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"85230039","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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