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Identifying key structures from social networks that aims to discover hidden patterns and extract valuable information is an essential task in the network analysis realm. These different structure detection tasks can be integrated naturally owing to the topological nature of key structures. However, identifying key network structures in most studies has been performed independently, leading to huge computational overheads. To address this challenge, this paper proposes a novel approach for handling key structures identification tasks simultaneously under the unified Formal Concept Analysis (FCA) framework. Specifically, we first implement the FCA-based representation of a social network and then generate the fine-grained knowledge representation, namely concept. Then, an efficient concept interestingness calculation algorithm suitable for social network scenarios is proposed. Next, we then leverage concept interestingness to quantify the hidden relations between concepts and network structures. Finally, an efficient algorithm for jointly key structures detection is developed based on constructed mapping relations. Extensive experiments conducted on real-world networks demonstrate that the efficiency and effectiveness of our proposed approach.
Jie Gao; Fei Hao; Zheng Pei; Geyong Min. Learning Concept Interestingness for Identifying Key Structures from Social Networks. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.
AMA StyleJie Gao, Fei Hao, Zheng Pei, Geyong Min. Learning Concept Interestingness for Identifying Key Structures from Social Networks. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.
Chicago/Turabian StyleJie Gao; Fei Hao; Zheng Pei; Geyong Min. 2021. "Learning Concept Interestingness for Identifying Key Structures from Social Networks." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.
Identifying the optimal groups of users that are closely connected and satisfy some ranking criteria from an attributed social network attracts significant attention from both academia and industry. Skyline query processing, a multicriteria decision-making optimized technique, is recently embedded into cohesive subgraphs mining in graphs/social networks. However, the existing studies cannot capture the fuzzy property of connections between users in social networks. To fill this gap, in this article, we formulate a novel model of the skyline (λ,k)-cliques over a fuzzy attributed social network and develop a formal concept analysis (FCA)-based skyline (λ,k)-cliques identification algorithm. Specifically, λ can be regarded as a quality control parameter for measuring the stability of the cohesive groups. Extensive experimental results conducted on three real-world datasets demonstrate the effectiveness of the skyline (λ,k)-clique model in a fuzzy attributed social network. Furthermore, an illustrative example is executed for revealing the usefulness of our model. It is expected that our proposed skyline (λ,k)-clique model can be widely used in various graph-based computational social systems, such as optimal team formation in crowdsourcing, and group recommendation in social networks.
Fei Hao; Jie Gao; Jianrui Chen; Aziz Nasridinov; Geyong Min. Skyline (λ,k)-Cliques Identification From Fuzzy Attributed Social Networks. IEEE Transactions on Computational Social Systems 2021, PP, 1 -12.
AMA StyleFei Hao, Jie Gao, Jianrui Chen, Aziz Nasridinov, Geyong Min. Skyline (λ,k)-Cliques Identification From Fuzzy Attributed Social Networks. IEEE Transactions on Computational Social Systems. 2021; PP (99):1-12.
Chicago/Turabian StyleFei Hao; Jie Gao; Jianrui Chen; Aziz Nasridinov; Geyong Min. 2021. "Skyline (λ,k)-Cliques Identification From Fuzzy Attributed Social Networks." IEEE Transactions on Computational Social Systems PP, no. 99: 1-12.
Knowledge graph describes entities by numerous RDF data (subject-predicate-object triples), which has been widely applied in various fields, such as artificial intelligence, Semantic Web, entity summarization. With time elapses, the continuously increasing RDF descriptions of entity lead to information overload and further cause people confused. With this backdrop, automatic entity summarization has received much attention in recent years, aiming to select the most concise and most typical facts that depict an entity in brief from lengthy RDF data. As new descriptions of entity are continually coming, creating a compact summary of entity quickly from a lengthy knowledge graph is challenging. To address this problem, this paper firstly formulates the problem and proposes a novel approach of Incremental Entity Summarization by leveraging Formal Concept Analysis (FCA), called IES-FCA. Additionally, we not only prove the rationality of our suggested method mathematically, but also carry out extensive experiments using two real-world datasets. The experimental results demonstrate that the proposed method IES-FCA can save about 8.7% of time consumption for all entities than the non-incremental entity summarization approach KAFCA at best. As for the effectiveness, IES-FCA outperforms the state-of-the-art algorithms in terms of F1-measure, MAP, and NDCG.
Erhe Yang; Fei Hao; Yixuan Yang; Carmen De Maio; Aziz Nasridinov; Geyong Min; Laurence T. Yang. Incremental Entity Summarization with Formal Concept Analysis. IEEE Transactions on Services Computing 2021, PP, 1 -1.
AMA StyleErhe Yang, Fei Hao, Yixuan Yang, Carmen De Maio, Aziz Nasridinov, Geyong Min, Laurence T. Yang. Incremental Entity Summarization with Formal Concept Analysis. IEEE Transactions on Services Computing. 2021; PP (99):1-1.
Chicago/Turabian StyleErhe Yang; Fei Hao; Yixuan Yang; Carmen De Maio; Aziz Nasridinov; Geyong Min; Laurence T. Yang. 2021. "Incremental Entity Summarization with Formal Concept Analysis." IEEE Transactions on Services Computing PP, no. 99: 1-1.
Three-way concept analysis (3WCA) has been an emerging and important methodology for knowledge discovery and data analysis. Particularly, 3WCA can efficiently characterize the information of “jointly possessed” and “jointly not possessed” compared to the classical formal concept only can describe common attributes owned by objects. This property, typical of 3WCA has a huge potential in the field of Natural Language Generation (NLG). However, the construction of a three-way concept lattice is proved as an NP-complete problem and even harder than the construction of conventional concept lattice. This could negatively affect the use of 3WCA for NLG in real contexts. Hence, it is necessary to prune the three-way concept lattice and extract more interesting three-way concepts for knowledge acquisition. To this end, this paper defines the stability of a three-way concept and analyzes the relevant properties. An efficient computational algorithm for calculating the stability of three-way concepts is developed and evaluated by an experiment. In addition, a case study on NLG is conducted for demonstrating the applicability of the proposed technique.
Fei Hao; Jie Gao; Carmen Bisogni; Geyong Min; Vincenzo Loia; Carmen De Maio. Stability of three-way concepts and its application to natural language generation. Pattern Recognition Letters 2021, 149, 51 -58.
AMA StyleFei Hao, Jie Gao, Carmen Bisogni, Geyong Min, Vincenzo Loia, Carmen De Maio. Stability of three-way concepts and its application to natural language generation. Pattern Recognition Letters. 2021; 149 ():51-58.
Chicago/Turabian StyleFei Hao; Jie Gao; Carmen Bisogni; Geyong Min; Vincenzo Loia; Carmen De Maio. 2021. "Stability of three-way concepts and its application to natural language generation." Pattern Recognition Letters 149, no. : 51-58.
The booming of Social Internet of Things (SIoT) has witnessed the significance of graph mining and analysis for social network management. Online Social Networks (OSNs) can be efficiently managed by monitoring users behaviors within a cohesive social group represented by a maximal clique. They can further provide valued social intelligence for their users. Maximal Cliques Problem (MCP) as a fundamental problem in graph mining and analysis is to identify the maximal cliques in a graph. Existing studies on MCP mainly focus on static graphs. In this paper, we adopt the Formal Concept Analysis (FCA) theory to represent and analyze social networks. We then develop two novel formal concepts generation algorithms, termed Add-FCA and Dec-FCA, that can be applicable to OSNs for detecting the maximal cliques and characterizing the dynamic evolution process of maximal cliques in OSNs. Extensive experimental results are conducted to investigate and demonstrate the correctness and effectiveness of the proposed algorithms. The results reveal that our algorithms can efficiently capture and manage the evolutionary patterns of maximal cliques in OSNs, and a quantitative relation among them is presented. In addition, an illustrative example is presented to verify the usefulness of the proposed approach.
Yixuan Yang; Fei Hao; Beibei Pang; Geyong Min; Yulei Wu. Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach. IEEE Transactions on Network Science and Engineering 2021, PP, 1 -1.
AMA StyleYixuan Yang, Fei Hao, Beibei Pang, Geyong Min, Yulei Wu. Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach. IEEE Transactions on Network Science and Engineering. 2021; PP (99):1-1.
Chicago/Turabian StyleYixuan Yang; Fei Hao; Beibei Pang; Geyong Min; Yulei Wu. 2021. "Dynamic Maximal Cliques Detection and Evolution Management in Social Internet of Things: A Formal Concept Analysis Approach." IEEE Transactions on Network Science and Engineering PP, no. 99: 1-1.
Citation recommendation systems mainly help researchers find the lists of references that related to their interests effectively and automatically. The existing approaches face the issues of data sparsity and high-dimensional in large-scale bibliographic network representation, which hinder the citation recommendation performance. To address these problems, we proposed a Content-Sensitive citation representation approach for Citation Recommendation, named CSCR. Firstly, the Doc2vec model is used to generate a paper embedding according to paper content. Then, utilizing the similarity between the paper content embeddings to select the assumed neighbours of the target paper, append the auxiliary links between target paper and its new neighbours in the bibliographic network. Thirdly, distributed network representation method is implemented on appended bibliographic network to obtain the paper node embedding, which can learn interpretable lower dimension embedding for paper nodes. Finally, the embedding vectors of these papers can be used to conduct citation recommendation. Experimental results show that the proposed approach significantly outperforms other benchmark methods in Normalized Discounted Cumulative Gain (NDCG) and the positive rate (Recall).
Lantian Guo; Xiaoyan Cai; Haohua Qin; Fei Hao; Sensen Guo. A content-sensitive citation representation approach for citation recommendation. Journal of Ambient Intelligence and Humanized Computing 2021, 1 -12.
AMA StyleLantian Guo, Xiaoyan Cai, Haohua Qin, Fei Hao, Sensen Guo. A content-sensitive citation representation approach for citation recommendation. Journal of Ambient Intelligence and Humanized Computing. 2021; ():1-12.
Chicago/Turabian StyleLantian Guo; Xiaoyan Cai; Haohua Qin; Fei Hao; Sensen Guo. 2021. "A content-sensitive citation representation approach for citation recommendation." Journal of Ambient Intelligence and Humanized Computing , no. : 1-12.
Edge computing provides cloud services at the edge of the network for Internet of Things (IoT) devices. It aims to address low latency of the network and alleviates data processing of the cloud. This “cloud-edge-device” paradigm brings convenience as well as challenges for location-privacy protection of the IoT. In the edge computing environment, the fixed edge equipment supplies computing services for adjacent IoT devices. Therefore, edge computing suffers location leakage as the connection and authentication records imply the location of IoT devices. This article focuses on the location awareness in the edge computing environment. We adopt the “deniability” of authentication to prevent location leakage when IoT devices connect to the edge nodes. In our solution, an efficient deniable authentication based on a two-user ring signature is constructed. The robustness of authentication makes the fixed edge equipment accept the legal end devices. Besides, the deniability of authentication cannot convince any third party that the fact of this authentication occurred as communication transcript is no longer an evidence for this connection. Therefore, it handles the inherent location risk in edge computing. Compared to efficient deniable authentications, our protocol saves 10.728% and 14.696% computational cost, respectively.
Shengke Zeng; Hongjie Zhang; Fei Hao; Hongwei Li. Deniable-Based Privacy-Preserving Authentication Against Location Leakage in Edge Computing. IEEE Systems Journal 2021, PP, 1 -10.
AMA StyleShengke Zeng, Hongjie Zhang, Fei Hao, Hongwei Li. Deniable-Based Privacy-Preserving Authentication Against Location Leakage in Edge Computing. IEEE Systems Journal. 2021; PP (99):1-10.
Chicago/Turabian StyleShengke Zeng; Hongjie Zhang; Fei Hao; Hongwei Li. 2021. "Deniable-Based Privacy-Preserving Authentication Against Location Leakage in Edge Computing." IEEE Systems Journal PP, no. 99: 1-10.
Mobile CrowdSensing (MCS) has emerged as a novel paradigm for performing large-scale sensing tasks. Many incentive mechanisms have been proposed to encourage user participation in MCS. However, most of them ignore the inevitable cold start stage of MCS, where the MCS system has just begun releasing tasks. Also, they all adopt the single-round incentive without considerations of the continuous cumulative effect. Given the severe shortage of participants in the cold start stage of MCS, this paper proposes a Multi-Round Incentive Mechanism (MRIM). MRIM is based on monetary incentives by adopting multi-round cooperation and alternating between task information diffusion and task allocation operations, both of which are NP-hard problems even without inter-round coupling imposed by system budget constraints. We explore a method to predict the probability of users participating in tasks accurately. Furthermore, we present an efficient task information diffusion algorithm to maximize the number of users participating in tasks by submitting bids. We propose a fast task allocation algorithm based on truthful auction, comprising an approximation algorithm for solving the one-round winner selection and payment calculation. With budget constraints, MRIM maximizes the number of completed tasks by iteratively performing task information diffusion and task allocation. We also prove that MRIM also possesses desired properties such as computational efficiency, user rationality, platform profitability, and price truthfulness, which can further guarantee the robustness of MRIM. The extensive simulations conducted on real-world datasets have proved the efficiency of MRIM.
Yaguang Lin; Zhipeng Cai; XiaoMing Wang; Fei Hao; Liang Wang; Akshita Maradapu Vera Venkata Sai. Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing. IEEE Transactions on Vehicular Technology 2021, 70, 993 -1007.
AMA StyleYaguang Lin, Zhipeng Cai, XiaoMing Wang, Fei Hao, Liang Wang, Akshita Maradapu Vera Venkata Sai. Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing. IEEE Transactions on Vehicular Technology. 2021; 70 (1):993-1007.
Chicago/Turabian StyleYaguang Lin; Zhipeng Cai; XiaoMing Wang; Fei Hao; Liang Wang; Akshita Maradapu Vera Venkata Sai. 2021. "Multi-Round Incentive Mechanism for Cold Start-Enabled Mobile Crowdsensing." IEEE Transactions on Vehicular Technology 70, no. 1: 993-1007.
Considerable attention has recently been devoted to Knowledge Graph (KG), which has been applied in many domains. However, the information is often imprecise and vague when constructing the knowledge graph and thus the Fuzzy Knowledge Graph (FKG) emerged. Considering the increasing data in FKG, this paper firstly formulates the entity summarization in FKG and proposes an approach leveraging Fuzzy Formal Concept Analysis (FFCA). More specifically, the predicates and objects in RDF triples are deemed as attributes and objects in FFCA, respectively. Then, the fuzzy formal context can be obtained and the fuzzy concept lattice can be constructed. Finally, the concepts are ranked by the cardinality of the extent in concept lattice and the vague value of objects in RDF triples.
Erhe Yang; Fei Hao; Jie Gao; Doo-Soon Park. Entity Summarization in Fuzzy Knowledge Graph Based on Fuzzy Concept Analysis. Lecture Notes in Electrical Engineering 2020, 19 -24.
AMA StyleErhe Yang, Fei Hao, Jie Gao, Doo-Soon Park. Entity Summarization in Fuzzy Knowledge Graph Based on Fuzzy Concept Analysis. Lecture Notes in Electrical Engineering. 2020; ():19-24.
Chicago/Turabian StyleErhe Yang; Fei Hao; Jie Gao; Doo-Soon Park. 2020. "Entity Summarization in Fuzzy Knowledge Graph Based on Fuzzy Concept Analysis." Lecture Notes in Electrical Engineering , no. : 19-24.
The concept stability measure under the Formal Concept Analysis (FCA) theory is useful for improving the accuracy of structure identification of social networks. Nevertheless, the stability calculation is an NP-complete task which is the primary challenges in practical. Most existing studies have focused on the approximate estimate to calculate the stability. Therefore, we focus on introducing the Maximal Non-Generator-based Stability Calculation (MNG-SC) algorithm that directly deals with accurate stability calculation to pave the way for FCA’s application in structures identification of social networks. Specifically, a novel perspective of stability calculation by linking it to Maximal Non-Generator (MNG) is first provided. Then, the equivalence between maximal non-generator and lower neighbor concept is first proved, which greatly improves scalability and reduces computational complexity. The performed experiments show that the MNG-SC outperforms the pioneering approaches of the literature. Furthermore, a case study of identifying abnormal users in social networks is presented, which demonstrates the effectiveness and potential application of our algorithm.
Jie Gao; Fei Hao; Doo-Soon Park. On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services. Applied Sciences 2020, 10, 8618 .
AMA StyleJie Gao, Fei Hao, Doo-Soon Park. On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services. Applied Sciences. 2020; 10 (23):8618.
Chicago/Turabian StyleJie Gao; Fei Hao; Doo-Soon Park. 2020. "On the Computation of Concept Stability Based on Maximal Non-Generator for Social Networking Services." Applied Sciences 10, no. 23: 8618.
The film comments data contains a huge amount of mining research value, and text mining analysis of the animated film’s comments can objectively reflect the quality of the animated film presentation and the problems generally expressed by the audience. However, these film comments are often mixed. The existing well-known film reviews websites have not excavated typical reviews on the user’s film comment text, so neither the audience nor the animation creators can analyze and apply the comments.This paper presents a general framework for mining typical opinions of film comments and uses crawler technology to obtain network review data, extract comment keywords based on the TF-IDF algorithm, and convert comments segmentation into word vectors trained by a neural network through Word2Vec. Then, using certain extraction rules and the K-means algorithm, the typical opinions with the same semantics but different expressions are aggregated together, and the typical opinions of the animation review of “Monkey King: Hero Is Back” are excavated. From the excavated information, we find out the production problems of the animation, so as to provide a certain reference to the creation of animated movies.
Ting Wu; Fei Hao; Mijin Kim. Typical opinions mining based on Douban film comments in animated movies. Entertainment Computing 2020, 36, 100391 .
AMA StyleTing Wu, Fei Hao, Mijin Kim. Typical opinions mining based on Douban film comments in animated movies. Entertainment Computing. 2020; 36 ():100391.
Chicago/Turabian StyleTing Wu; Fei Hao; Mijin Kim. 2020. "Typical opinions mining based on Douban film comments in animated movies." Entertainment Computing 36, no. : 100391.
The development of mobile edge computing (MEC) is accelerating the popularity of 5G applications. In the 5G era, aiming to reduce energy consumption and latency, most applications or services are conducted on both edge cloud servers and cloud servers. However, the existing multi-cloud composition recommendation approaches are studied in the context of resources provided by a single cloud or multiple clouds. Hence, these approaches cannot cope with services requested by the composition of multiple clouds and edge clouds jointly in MEC. To this end, this paper firstly expands the structure of the multi-cloud service system and further constructs a multi-cloud multi-edge cloud (MCMEC) environment. Technically, we model this problem with formal concept analysis (FCA) by building the service–provider lattice and provider–cloud lattice, and select the candidate cloud composition that satisfies the user’s requirements. In order to obtain an optimized cloud combination that can efficiently reduce the energy consumption, money cost, and network latency, the skyline query mechanism is utilized for extracting the optimized cloud composition. We evaluate our approach by comparing the proposed algorithm to the random-based service composition approach. A case study is also conducted for demonstrating the effectiveness and superiority of our proposed approach.
Beibei Pang; Fei Hao; Doo-Soon Park; Carmen De Maio. A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing. Sustainability 2020, 12, 7661 .
AMA StyleBeibei Pang, Fei Hao, Doo-Soon Park, Carmen De Maio. A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing. Sustainability. 2020; 12 (18):7661.
Chicago/Turabian StyleBeibei Pang; Fei Hao; Doo-Soon Park; Carmen De Maio. 2020. "A Multi-Criteria Multi-Cloud Service Composition in Mobile Edge Computing." Sustainability 12, no. 18: 7661.
Due to the extensive use of unmanned aerial vehicles (UAVs) in civil and military environment, effective deployment and scheduling of a swarm of UAVs are rising to be a challenging issue in edge computing. This is especially apparent in the area of Internet of Things (IoT) where massive UAVs are connected for communications. One of the characteristics of IoT is that an operator can interact with more than one UAVs for the effective scheduling under multi-task requests. Based on this scenario, we clarify the issue on how to maintain the energy efficiency of UAVs and guarantee the reputation gain during the scheduling deployment. In this paper, we first formulate the energy consumption and reputation into the decision model of UAVs scheduling. A game-theoretic scheme is then developed for the optimal decision searching. With the developed model, a range of important parameters of UAV scheduling are thoroughly investigated. Our numerical results show that the proposed scheduling strategy is able to increase the reputation and decrease the energy consumption of UAVs simultaneously. In addition, in the game process, the profit of an operator can be maximized and the network economy research can be explored.
Juan Zhang; Yulei Wu; Geyong Min; Fei Hao; Laizhong Cui. Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing. IEEE Transactions on Cognitive Communications and Networking 2020, 6, 1204 -1217.
AMA StyleJuan Zhang, Yulei Wu, Geyong Min, Fei Hao, Laizhong Cui. Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing. IEEE Transactions on Cognitive Communications and Networking. 2020; 6 (4):1204-1217.
Chicago/Turabian StyleJuan Zhang; Yulei Wu; Geyong Min; Fei Hao; Laizhong Cui. 2020. "Balancing Energy Consumption and Reputation Gain of UAV Scheduling in Edge Computing." IEEE Transactions on Cognitive Communications and Networking 6, no. 4: 1204-1217.
With advanced development of Internet communication and ubiquitous computing, Social Networks are providing an important information channel for smart city construction. Therefore, analyzing Location-based Social Network is a very valuable work in achieving reasonable urban zoning. In Social Networks, a main purpose of prestige assessment is to extract influential users who are regarded as the key nodes for community detection from Onine Social Networks (OSNs). However, social relationships of users are rarely used to evaluate the popularity of physical locations and zone physical locations. In order to achieve urban area function zoning by evaluating the prestige of geographic regions based on user relationships in Location based Social Networks (LBSNs), this paper proposes a Prestige Density-Based Spatial Clustering of Applications with Noise algorithm (P-DBSCAN) by improving the existing DBSCAN algorithm. Specifically, the algorithm first calculates the centrality of users in the social network, and then converts the centrality of users into the location-centrality through the users’ check-in data. After the centrality of each location is obtained, the discrete locations are clustered according to four constraints of the given radius. After clustering, the result of urban area function zoning can be achieved. Extensive experiments are conducted for demonstrating the effectiveness of our proposed algorithm in this paper. In addition, the visualization results reveal the correctness of our proposed approach.
Fei Hao; Junzhe Zhang; Zongtao Duan; Liang Zhao; Lantian Guo; Doo-Soon Park. Urban Area Function Zoning Based on User Relationships in Location-Based Social Networks. IEEE Access 2020, 8, 23487 -23495.
AMA StyleFei Hao, Junzhe Zhang, Zongtao Duan, Liang Zhao, Lantian Guo, Doo-Soon Park. Urban Area Function Zoning Based on User Relationships in Location-Based Social Networks. IEEE Access. 2020; 8 (99):23487-23495.
Chicago/Turabian StyleFei Hao; Junzhe Zhang; Zongtao Duan; Liang Zhao; Lantian Guo; Doo-Soon Park. 2020. "Urban Area Function Zoning Based on User Relationships in Location-Based Social Networks." IEEE Access 8, no. 99: 23487-23495.
Mobile social networks (MSNs) provide real-time information services to individuals in social communities through mobile devices. However, due to their high openness and autonomy, MSNs have been suffering from rampant rumors, fraudulent activities, and other types of misuses. To mitigate such threats, it is urgent to control the spread of fraud information. The research challenge is: how to design control strategies to efficiently utilize limited resources and meanwhile minimize individuals' losses caused by fraud information? To this end, we model the fraud information control issue as an optimal control problem, in which the control resources consumption for implementing control strategies and the losses of individuals are jointly taken as a constraint called total cost, and the minimum total cost becomes the objective function. Based on the optimal control theory, we devise the optimal dynamic allocation of control strategies. Besides, a dynamics model for fraud information diffusion is established by considering the uncertain mental state of individuals, we investigate the trend of fraud information diffusion and the stability of the dynamics model. Our simulation study shows that the proposed optimal control strategies can effectively inhibit the diffusion of fraud information while incurring the smallest total cost. Compared with other control strategies, the control effect of the proposed optimal control strategies is about 10% higher.
Yaguang Lin; XiaoMing Wang; Fei Hao; Yichuan Jiang; Yulei Wu; Geyong Min; Daojing He; Sencun Zhu; Wei Zhao. Dynamic Control of Fraud Information Spreading in Mobile Social Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems 2019, 51, 3725 -3738.
AMA StyleYaguang Lin, XiaoMing Wang, Fei Hao, Yichuan Jiang, Yulei Wu, Geyong Min, Daojing He, Sencun Zhu, Wei Zhao. Dynamic Control of Fraud Information Spreading in Mobile Social Networks. IEEE Transactions on Systems, Man, and Cybernetics: Systems. 2019; 51 (6):3725-3738.
Chicago/Turabian StyleYaguang Lin; XiaoMing Wang; Fei Hao; Yichuan Jiang; Yulei Wu; Geyong Min; Daojing He; Sencun Zhu; Wei Zhao. 2019. "Dynamic Control of Fraud Information Spreading in Mobile Social Networks." IEEE Transactions on Systems, Man, and Cybernetics: Systems 51, no. 6: 3725-3738.
Yaguang Lin; Zhipeng Cai; XiaoMing Wang; Fei Hao. Incentive Mechanisms for Crowdblocking Rumors in Mobile Social Networks. IEEE Transactions on Vehicular Technology 2019, 68, 9220 -9232.
AMA StyleYaguang Lin, Zhipeng Cai, XiaoMing Wang, Fei Hao. Incentive Mechanisms for Crowdblocking Rumors in Mobile Social Networks. IEEE Transactions on Vehicular Technology. 2019; 68 (9):9220-9232.
Chicago/Turabian StyleYaguang Lin; Zhipeng Cai; XiaoMing Wang; Fei Hao. 2019. "Incentive Mechanisms for Crowdblocking Rumors in Mobile Social Networks." IEEE Transactions on Vehicular Technology 68, no. 9: 9220-9232.
With the rapid development of the Internet, the data generated from application platforms such as online shopping, e-education, and digital entertainment has exhibited dramatical growth, which has caused serious information overload to Internet users. The traditional recommendation approaches are crucial for Internet users to extract valuable information from various information. However, there exist some problems such as sparse data, cold start, and over-reliance on manual extracted feature and so on. To address the above problems, this paper proposes a novel recommender with Attention-based Convolutional Neural Network and Factorization Machines (ACNN-FM), which achieves the recommendation with comments. Firstly, from the perspective of local to overall, this paper proposes a word-level attention mechanism and a phrase-level attention mechanism to increase the ability to remember the importance and the order of historical vocabulary (phrase) in the process of text processing of convolutional neural networks. Secondly, it constructs a model to automatically extract hidden features of users and items from comments in the form of natural language. Finally, we utilize factorization machines to analyze the association between the hidden features of users and items, and implement the recommendation based on the association. Extensive experiments are conducted for demonstrating that ACNN-FM method outperforms state-of-the-art NARR method, and ACNN-FM has the highest data utilization among NARR, DeepCoNN, BCF and NMF methods, thus the recommendation performance is significantly improved in large-scale data environment.
Guangyao Pang; XiaoMing Wang; Fei Hao; Jiehang Xie; Xinyan Wang; Yaguang Lin; Xueyang Qin. ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines. Knowledge-Based Systems 2019, 181, 104786 .
AMA StyleGuangyao Pang, XiaoMing Wang, Fei Hao, Jiehang Xie, Xinyan Wang, Yaguang Lin, Xueyang Qin. ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines. Knowledge-Based Systems. 2019; 181 ():104786.
Chicago/Turabian StyleGuangyao Pang; XiaoMing Wang; Fei Hao; Jiehang Xie; Xinyan Wang; Yaguang Lin; Xueyang Qin. 2019. "ACNN-FM: A novel recommender with attention-based convolutional neural network and factorization machines." Knowledge-Based Systems 181, no. : 104786.
Quality of Service (QoS) value is usually unknown in service recommendation practice. There are some matrix factorization approaches for predicting the unknown value with a user-service model, which uses a single collaboration with the user’s neighbor when looking for different services. However, the QoS value is highly related to the service provider and participants. The services are considered in various collaboration based on different users. By considering the context of services, this paper proposes a QoS prediction model using tensor decomposition based on service collaboration, called Service-oriented Tensor (SOT). The prediction approach analyzes service collaboration from other similar services and relevant users by using a three-order tensor. Compared with the traditional model, the experiment results show that the proposed model achieves better prediction accuracy.
Lantian Guo; Dejun Mu; Xiaoyan Cai; Gang Tian; Fei Hao. Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model. IEEE Access 2019, 7, 55721 -55731.
AMA StyleLantian Guo, Dejun Mu, Xiaoyan Cai, Gang Tian, Fei Hao. Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model. IEEE Access. 2019; 7 (99):55721-55731.
Chicago/Turabian StyleLantian Guo; Dejun Mu; Xiaoyan Cai; Gang Tian; Fei Hao. 2019. "Personalized QoS Prediction for Service Recommendation With a Service-Oriented Tensor Model." IEEE Access 7, no. 99: 55721-55731.
Mobile Edge Computing (MEC) is providing cloud computing capabilities within the radio access networks and offering a new paradigm to liberate the mobile devices from heavy computational workloads. Importantly, MEC can effectively reduce latency, avoid congestion and prolong the battery lifetime of mobile devices by offloading the computation tasks from the mobile devices to a physically proximal MEC servers. Particularly, Virtual Machines (VMs) scheduling is a critical issue for tasks offloading and computation in MEC. Regarding to the VMs scheduling problem in MEC environmnet, this paper pioneers the use of Formal Concept Analysis (FCA) methodology for identifying the mapping from tasks to VMs. Specifically, the VMs profile and tasks descriptions are initially characterized as the formal contexts, respectively. With the constructed formal contexts, the corresponding formal concepts which refer to the rules set, are then generated. To better infuse the rules set of VMs and tasks, this paper defines a similarity measurement between formal concepts of VMs and tasks. Consequently, the matching problem from a given task to a virtual machine is to return the expected virtual machine according to the principle of maximum similarity degree between formal concepts of virtual machine and task. Extensive simulations are conducted with a real dataset for the validation of feasibility and effectiveness of the proposed approach. Specifically, the proposed approach can significantly reduce the energy consumption around 28% comparing to the approach without consideration of energy consumption. Overall, It is demonstrated that FCA-based VMs scheduling is a novel solution for a sustainable VMs scheduling in MEC environment.
Fei Hao; Guangyao Pang; Zheng Pei; Ke Yun Qin; Yu Zhang; XiaoMing Wang. Virtual Machines Scheduling in Mobile Edge Computing: A Formal Concept Analysis Approach. IEEE Transactions on Sustainable Computing 2019, 5, 319 -328.
AMA StyleFei Hao, Guangyao Pang, Zheng Pei, Ke Yun Qin, Yu Zhang, XiaoMing Wang. Virtual Machines Scheduling in Mobile Edge Computing: A Formal Concept Analysis Approach. IEEE Transactions on Sustainable Computing. 2019; 5 (3):319-328.
Chicago/Turabian StyleFei Hao; Guangyao Pang; Zheng Pei; Ke Yun Qin; Yu Zhang; XiaoMing Wang. 2019. "Virtual Machines Scheduling in Mobile Edge Computing: A Formal Concept Analysis Approach." IEEE Transactions on Sustainable Computing 5, no. 3: 319-328.
Linguistic decision making is an important subject in decision making, many interesting and important linguistic decision making methods have been proposed, in which, alternatives-criteria decision matrix are uniformly used to express linguistic assessments of alternatives provided by decision makers with respect to criteria. Alternatives-criteria decision matrixes have some limitations when we use them to distinguish distinct, partial unknown or hesitant linguistic decision making or carry out linguistic decision making in the huge amounts of decision information and alternatives. In this paper, we propose alternatives-linguistic terms decision matrix to represent linguistic assessments of alternatives, analyze advantages of the decision matrix in representing linguistic assessments and distinguishing distinct, partial unknown or hesitant linguistic decision making. To simple and fast fuse alternatives-linguistic terms decision matrixes, we further provide linguistic multiset or fuzzy linguistic multiset to represent linguistic assessments in alternatives-linguistic terms decision matrixes, analyze the function properties of the fuzzy linguistic multiset. Motivated by fuzzy multiset and the TOPSIS method, we develop the fuzzy linguistic multiset TOPSIS method for linguistic decision making, the method is mainly consisted of transformation, aggregation and exploitation phases. In transformation phase, linguistic assessments of alternatives are transformed into fuzzy linguistic multisets by using alternatives-linguistic terms decision matrixes. In aggregation phase, we use Union, Intersection and Sum operations of multisets to obtain the positive and negative ideal solutions of linguistic decision making, which are different with the positive and negative ideal solutions of the traditional TOPSIS method, in addition, we provide a pseudo-distance between two fuzzy linguistic multisets to fast fuse linguistic assessments of alternatives. In exploitation phase, we define a new closeness degree of alternative by using pseudo-distances between the alternative and the positive and negative ideal solutions, which can be used to obtain the set of most satisfying alternatives. We also design an algorithm to carry out linguistic decision making based on the proposed method. In cases study, we use two practical examples to illustrate the practicality of the proposed method and compare it with the symbolic aggregation-based method, the hesitant fuzzy linguistic TOPSIS method, the hesitant fuzzy linguistic VIKOR method and the probabilistic linguistic term sets TOPSIS method, results indicate that alternatives-linguistic terms decision matrix and fuzzy linguistic multiset are alternative, useful and flexible tools for linguistic decision method and the fuzzy linguistic multiset TOPSIS method is suitable to deal with partial unknown or hesitant linguistic decision making.
Zheng Pei; Jing Liu; Fei Hao; Bin Zhou. FLM-TOPSIS: The fuzzy linguistic multiset TOPSIS method and its application in linguistic decision making. Information Fusion 2019, 45, 266 -281.
AMA StyleZheng Pei, Jing Liu, Fei Hao, Bin Zhou. FLM-TOPSIS: The fuzzy linguistic multiset TOPSIS method and its application in linguistic decision making. Information Fusion. 2019; 45 ():266-281.
Chicago/Turabian StyleZheng Pei; Jing Liu; Fei Hao; Bin Zhou. 2019. "FLM-TOPSIS: The fuzzy linguistic multiset TOPSIS method and its application in linguistic decision making." Information Fusion 45, no. : 266-281.