- Jie Wu;Ning Wang;
Social Influence Maximization Problems(SIMPs) deal with selecting k seeds in a given Online Social Network(OSN) to maximize the number of eventually-influenced users.This is done by using these seeds based on a given set of influence probabilities among neighbors in the OSN.Although the SIMP has been proved to be NP-hard,it has both submodular(with a natural diminishing-return) and monotone(with an increasing influenced users through propagation) that make the problem suitable for approximation solutions.However,several special SIMPs cannot be modeled as submodular or monotone functions.In this paper,we look at several conditions under which non-submodular or non-monotone functions can be handled or approximated.One is a profit-maximization SIMP where seed selection cost is included in the overall utility function,breaking the monotone property.The other is a crowd-influence SIMP where crowd influence exists in addition to individual influence,breaking the submodular property.We then review several new techniques and notions,including double-greedy algorithms and the supermodular degree,that can be used to address special SIMPs.Our main results show that for a specific SIMP model,special network structures of OSNs can help reduce its time complexity of the SIMP.
2020年06期 v.25 703-711页 [查看摘要][在线阅读][下载 1216K] [下载次数：24 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Jie Wu;Ning Wang;
Social Influence Maximization Problems(SIMPs) deal with selecting k seeds in a given Online Social Network(OSN) to maximize the number of eventually-influenced users.This is done by using these seeds based on a given set of influence probabilities among neighbors in the OSN.Although the SIMP has been proved to be NP-hard,it has both submodular(with a natural diminishing-return) and monotone(with an increasing influenced users through propagation) that make the problem suitable for approximation solutions.However,several special SIMPs cannot be modeled as submodular or monotone functions.In this paper,we look at several conditions under which non-submodular or non-monotone functions can be handled or approximated.One is a profit-maximization SIMP where seed selection cost is included in the overall utility function,breaking the monotone property.The other is a crowd-influence SIMP where crowd influence exists in addition to individual influence,breaking the submodular property.We then review several new techniques and notions,including double-greedy algorithms and the supermodular degree,that can be used to address special SIMPs.Our main results show that for a specific SIMP model,special network structures of OSNs can help reduce its time complexity of the SIMP.
2020年06期 v.25 703-711页 [查看摘要][在线阅读][下载 1216K] [下载次数：24 ] |[网刊下载次数：0 ] |[引用频次：5 ] |[阅读次数：0 ] - Lei Zhang;Chenbo Xu;Yihua Gao;Yi Han;Xiaojiang Du;Zhihong Tian;
In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world's most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero's match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM) neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW) model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.
2020年06期 v.25 712-720页 [查看摘要][在线阅读][下载 923K] [下载次数：177 ] |[网刊下载次数：0 ] |[引用频次：14 ] |[阅读次数：0 ] - Lei Zhang;Chenbo Xu;Yihua Gao;Yi Han;Xiaojiang Du;Zhihong Tian;
In recent years,e-sports has rapidly developed,and the industry has produced large amounts of data with specifications,and these data are easily to be obtained.Due to the above characteristics,data mining and deep learning methods can be used to guide players and develop appropriate strategies to win games.As one of the world's most famous e-sports events,Dota2 has a large audience base and a good game system.A victory in a game is often associated with a hero's match,and players are often unable to pick the best lineup to compete.To solve this problem,in this paper,we present an improved bidirectional Long Short-Term Memory(LSTM) neural network model for Dota2 lineup recommendations.The model uses the Continuous Bag Of Words(CBOW) model in the Word2 vec model to generate hero vectors.The CBOW model can predict the context of a word in a sentence.Accordingly,a word is transformed into a hero,a sentence into a lineup,and a word vector into a hero vector,the model applied in this article recommends the last hero according to the first four heroes selected first,thereby solving a series of recommendation problems.
2020年06期 v.25 712-720页 [查看摘要][在线阅读][下载 923K] [下载次数：177 ] |[网刊下载次数：0 ] |[引用频次：14 ] |[阅读次数：0 ] - Jie Wu;
In recent years,the booming of the Bike Sharing System(BSS) has played an important role in offering a convenient means of public transport.The BSS is also viewed as a solution to the first/last mile connection issue in urban cities.The BSS can be classified into dock and dock-less.However,due to imbalance in bike usage over spatial and temporal domains,stations in the BSS may exhibit overflow(full stations) or underflow(empty stations).In this paper,we will take a holistic view of the BSS design by examining the following four components:system design,system prediction,system balancing,and trip advisor.We will focus on system balancing,addressing the issue of overflow/underflow.We will look at two main methods of bike re-balancing:with trucks and with workers.Discussion on the other three components that are related to system balancing will also be given.Specifically,we will study various algorithmic solutions with the availability of data in spacial and temporal domains.Finally,we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.
2020年06期 v.25 721-733页 [查看摘要][在线阅读][下载 1667K] [下载次数：48 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Jie Wu;
In recent years,the booming of the Bike Sharing System(BSS) has played an important role in offering a convenient means of public transport.The BSS is also viewed as a solution to the first/last mile connection issue in urban cities.The BSS can be classified into dock and dock-less.However,due to imbalance in bike usage over spatial and temporal domains,stations in the BSS may exhibit overflow(full stations) or underflow(empty stations).In this paper,we will take a holistic view of the BSS design by examining the following four components:system design,system prediction,system balancing,and trip advisor.We will focus on system balancing,addressing the issue of overflow/underflow.We will look at two main methods of bike re-balancing:with trucks and with workers.Discussion on the other three components that are related to system balancing will also be given.Specifically,we will study various algorithmic solutions with the availability of data in spacial and temporal domains.Finally,we will discuss several key challenges and opportunities of the BSS design and applications as well as the future of dock and dock-less BSS in a bigger setting of the transportation system.
2020年06期 v.25 721-733页 [查看摘要][在线阅读][下载 1667K] [下载次数：48 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Xinfang Song;Wei Jiang;Xiaohui Liu;Hui Lu;Zhihong Tian;Xiaojiang Du;
Social network services can not only help people form relationships and make new friends and partners,but also assist in processing personal information,sharing knowledge,and managing social relationships.Social networks achieve valuable communication and collaboration,bring additional business opportunities,and have great social value.Research on social network problems is effective by using assumption,definition,analysis,modeling,and optimization strategies.In this paper,we survey the existing problems of game theory applied to social networks and classify their application scenarios into four categories:information diffusion,behavior analysis,community detection,and information security.Readers can clearly master knowledge application in every category.Finally,we discuss certain limitations of game theory on the basis of research in recent years and propose future directions of social network research.
2020年06期 v.25 734-742页 [查看摘要][在线阅读][下载 963K] [下载次数：89 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Xinfang Song;Wei Jiang;Xiaohui Liu;Hui Lu;Zhihong Tian;Xiaojiang Du;
Social network services can not only help people form relationships and make new friends and partners,but also assist in processing personal information,sharing knowledge,and managing social relationships.Social networks achieve valuable communication and collaboration,bring additional business opportunities,and have great social value.Research on social network problems is effective by using assumption,definition,analysis,modeling,and optimization strategies.In this paper,we survey the existing problems of game theory applied to social networks and classify their application scenarios into four categories:information diffusion,behavior analysis,community detection,and information security.Readers can clearly master knowledge application in every category.Finally,we discuss certain limitations of game theory on the basis of research in recent years and propose future directions of social network research.
2020年06期 v.25 734-742页 [查看摘要][在线阅读][下载 963K] [下载次数：89 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Youssef Khazbak;Jingyao Fan;Sencun Zhu;Guohong Cao;
Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders' mobility information including the pick-up and drop-off location is tracked,the service provider can infer sensitive information about the riders such as where they live and work.To address these concerns,we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders' location privacy.We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location.However,with some side information,the service provider can launch location inference attacks.To overcome these attacks,we propose an enhanced scheme that allows a rider to specify his privacy preference.Novel techniques are designed to preserve rider's personalized privacy with limited loss of matching accuracy.Through trace-driven simulations,we compare our enhanced privacy preserving solution to existing work.Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution,while preserving personalized location privacy for riders.
2020年06期 v.25 743-757页 [查看摘要][在线阅读][下载 1743K] [下载次数：33 ] |[网刊下载次数：0 ] |[引用频次：6 ] |[阅读次数：0 ] - Youssef Khazbak;Jingyao Fan;Sencun Zhu;Guohong Cao;
Ride-hailing service has become a popular means of transportation due to its convenience and low cost.However,it also raises privacy concerns.Since riders' mobility information including the pick-up and drop-off location is tracked,the service provider can infer sensitive information about the riders such as where they live and work.To address these concerns,we propose location privacy preserving techniques that efficiently match riders and drivers while preserving riders' location privacy.We first propose a baseline solution that allows a rider to select the driver who is the closest to his pick-up location.However,with some side information,the service provider can launch location inference attacks.To overcome these attacks,we propose an enhanced scheme that allows a rider to specify his privacy preference.Novel techniques are designed to preserve rider's personalized privacy with limited loss of matching accuracy.Through trace-driven simulations,we compare our enhanced privacy preserving solution to existing work.Evaluation results show that our solution provides much better ride matching results that are close to the optimal solution,while preserving personalized location privacy for riders.
2020年06期 v.25 743-757页 [查看摘要][在线阅读][下载 1743K] [下载次数：33 ] |[网刊下载次数：0 ] |[引用频次：6 ] |[阅读次数：0 ] - Xiaoran Fan;Han Ding;Yanyong Zhang;Wade Trappe;Zhu Han;Rich Howard;
This paper presents a new Wireless Power Transfer(WPT) approach by aligning the phases of a group of spatially distributed Radio Frequency(RF) transmitters(TX) at the target receiver(RX) device.Our approach can transfer energy over tens of meters and even to targets blocked by obstacles.Compared to popular beamforming based WPTs,our approach leads to a drastically different energy density distribution:the energy density at the target receiver is much higher than the energy density at other locations.Due to this unique energy distribution pattern,our approach offers a safer WPT solution,which can be potentially scaled up to ship a higher level of energy over longer distances.Specifically,we model the energy density distribution and prove that our proposed system can create a high energy peak exactly at the target receiver.Then we conduct detailed simulation studies to investigate how the actual energy distribution is impacted by various important system parameters,including number/topology of transmitters,transmitter antenna directionality,the distance between receiver and transmitters,and environmental multipath.Finally,we build an actual prototype with 17 N210 and 4 B210 Universal Software Radio Peripheral(USRP) nodes,through which we validate the salient features and performance promises of the proposed system.
2020年06期 v.25 758-775页 [查看摘要][在线阅读][下载 2521K] [下载次数：41 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ] - Xiaoran Fan;Han Ding;Yanyong Zhang;Wade Trappe;Zhu Han;Rich Howard;
This paper presents a new Wireless Power Transfer(WPT) approach by aligning the phases of a group of spatially distributed Radio Frequency(RF) transmitters(TX) at the target receiver(RX) device.Our approach can transfer energy over tens of meters and even to targets blocked by obstacles.Compared to popular beamforming based WPTs,our approach leads to a drastically different energy density distribution:the energy density at the target receiver is much higher than the energy density at other locations.Due to this unique energy distribution pattern,our approach offers a safer WPT solution,which can be potentially scaled up to ship a higher level of energy over longer distances.Specifically,we model the energy density distribution and prove that our proposed system can create a high energy peak exactly at the target receiver.Then we conduct detailed simulation studies to investigate how the actual energy distribution is impacted by various important system parameters,including number/topology of transmitters,transmitter antenna directionality,the distance between receiver and transmitters,and environmental multipath.Finally,we build an actual prototype with 17 N210 and 4 B210 Universal Software Radio Peripheral(USRP) nodes,through which we validate the salient features and performance promises of the proposed system.
2020年06期 v.25 758-775页 [查看摘要][在线阅读][下载 2521K] [下载次数：41 ] |[网刊下载次数：0 ] |[引用频次：4 ] |[阅读次数：0 ]
- Oscar Pedreira;Félix García;Mario Piattini;Alejandro Cortinas;Ana Cerdeira-Pena;
Gamification has been applied in software engineering to improve quality and results by increasing people's motivation and engagement.A systematic mapping has identified research gaps in the field,one of them being the difficulty of creating an integrated gamified environment comprising all the tools of an organization,since most existing gamified tools are custom developments or prototypes.In this paper,we propose a gamification software architecture that allows us to transform the work environment of a software organization into an integrated gamified environment,i.e.,the organization can maintain its tools,and the rewards obtained by the users for their actions in different tools will mount up.We developed a gamification engine based on our proposal,and we carried out a case study in which we applied it in a real software development company.The case study shows that the gamification engine has allowed the company to create a gamified workplace by integrating custom-developed tools and off-the-shelf tools such as Redmine,TestLink,or JUnit,with the gamification engine.Two main advantages can be highlighted:(i) our solution allows the organization to maintain its current tools,and(ii) the rewards for actions in any tool accumulate in a centralized gamified environment.
2020年06期 v.25 776-797页 [查看摘要][在线阅读][下载 2631K] [下载次数：72 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Oscar Pedreira;Félix García;Mario Piattini;Alejandro Cortinas;Ana Cerdeira-Pena;
Gamification has been applied in software engineering to improve quality and results by increasing people's motivation and engagement.A systematic mapping has identified research gaps in the field,one of them being the difficulty of creating an integrated gamified environment comprising all the tools of an organization,since most existing gamified tools are custom developments or prototypes.In this paper,we propose a gamification software architecture that allows us to transform the work environment of a software organization into an integrated gamified environment,i.e.,the organization can maintain its tools,and the rewards obtained by the users for their actions in different tools will mount up.We developed a gamification engine based on our proposal,and we carried out a case study in which we applied it in a real software development company.The case study shows that the gamification engine has allowed the company to create a gamified workplace by integrating custom-developed tools and off-the-shelf tools such as Redmine,TestLink,or JUnit,with the gamification engine.Two main advantages can be highlighted:(i) our solution allows the organization to maintain its current tools,and(ii) the rewards for actions in any tool accumulate in a centralized gamified environment.
2020年06期 v.25 776-797页 [查看摘要][在线阅读][下载 2631K] [下载次数：72 ] |[网刊下载次数：0 ] |[引用频次：2 ] |[阅读次数：0 ] - Wei Jiang;Junyu Lin;Huiqiang Wang;Shichen Zou;
Semantic Service Matchmaking(SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.
2020年06期 v.25 798-812页 [查看摘要][在线阅读][下载 1701K] [下载次数：56 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Wei Jiang;Junyu Lin;Huiqiang Wang;Shichen Zou;
Semantic Service Matchmaking(SSM) can be leveraged for mining the most suitable service to accommodate a diversity of user demands.However,existing research on SSM mostly involves logical or non-logical matching,leading to unavoidable false-positive and false-negative problems.Combining different types of SSM methods is an effective way to improve this situation,but the adaptive combination of different service matching methods is still a difficult issue.To conquer this difficulty,a hybrid SSM method,which is based on a random forest and combines the advantages of existing SSM methods,is proposed in this paper.The result of each SSM method is treated as a multi-dimensional feature vector input for the random forest,converting the service matching into a two classification problem.Therefore,our method avoids the flaws found in manual threshold setting.Experimental results show that the proposed method achieves an outstanding performance.
2020年06期 v.25 798-812页 [查看摘要][在线阅读][下载 1701K] [下载次数：56 ] |[网刊下载次数：0 ] |[引用频次：10 ] |[阅读次数：0 ] - Ning Wang;Gege Guo;Baonan Wang;Chao Wang;
In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent "brain" to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA) and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA) to solve the problem of urban taxi-stand locations.Based on the traffic trajectory data of Xi'an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are compared.We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher robustness.QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.
2020年06期 v.25 813-825页 [查看摘要][在线阅读][下载 1522K] [下载次数：51 ] |[网刊下载次数：0 ] |[引用频次：9 ] |[阅读次数：0 ] - Ning Wang;Gege Guo;Baonan Wang;Chao Wang;
In recent years,the urbanization process has brought modernity while also causing key issues,such as traffic congestion and parking conflicts.Therefore,cities need a more intelligent "brain" to form more intelligent and efficient transportation systems.At present,as a type of machine learning,the traditional clustering algorithm still has limitations.K-means algorithm is widely used to solve traffic clustering problems,but it has limitations,such as sensitivity to initial points and poor robustness.Therefore,based on the hybrid architecture of Quantum Annealing(QA) and brain-inspired cognitive computing,this study proposes QA and Brain-Inspired Clustering Algorithm(QABICA) to solve the problem of urban taxi-stand locations.Based on the traffic trajectory data of Xi'an and Chengdu provided by Didi Chuxing,the clustering results of our algorithm and K-means algorithm are compared.We find that the average taxi-stand location bias of the final result based on QABICA is smaller than that based on K-means,and the bias of our algorithm can effectively reduce the tradition K-means bias by approximately 42%,up to approximately 83%,with higher robustness.QA algorithm is able to jump out of the local suboptimal solutions and approach the global optimum,and brain-inspired cognitive computing provides search feedback and direction.Thus,we will further consider applying our algorithm to analyze urban traffic flow,and solve traffic congestion and other key problems in intelligent transportation.
2020年06期 v.25 813-825页 [查看摘要][在线阅读][下载 1522K] [下载次数：51 ] |[网刊下载次数：0 ] |[引用频次：9 ] |[阅读次数：0 ]