Key Laboratory of Machine Intelligence and Advanced Computing

MOE, China

Key Laboratory of Machine Intelligence and Advanced Computing

MOE, China
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Zhang D.-C.,Sun Yat Sen University | Li M.,Sun Yat Sen University | Wang C.-D.,Key Laboratory of Machine Intelligence and Advanced Computing
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 | Year: 2016

Point of interest (POI) recommendation, a service which can help people discover useful and interesting locations has emerged rapidly with the development of location-based social networks (LBSNs), like Foursquare, Gowalla and Wechat. The large number of check-in histories make it possible to mine the preference of each user and then to provide accurate personalized POI recommendation. In real-world applications, apart from check-in data, there are some other useful information available for making better POI recommendation, such as social relationship among users and geographical influence. In this paper, a new POI recommendation method called Social and Geographical Fusing Model (SGFM) is designed. The basic idea is summarized as follows. Firstly, the users' check-in records and social influence are integrated in a combinative model. Then the global user impact factors generated by the PageRank algorithm are used to improve the combinative model. Secondly, a geographical influence measurement is used to capture the users' physical check-in characters. Finally, the enhanced combinative model and geographical influence are combined together to form a new framework. Extensive experiments have been conducted on a famous dataset, namely Gowalla. The comparison results confirm that the proposed framework outperforms state-of-the-art POI recommendation methods significantly. © 2016 IEEE.


Yang Y.-M.,Sun Yat Sen University | Wang C.-D.,Guangdong Key Laboratory of Information Security Technology | Lai J.-H.,Key Laboratory of Machine Intelligence and Advanced Computing
Proceedings - 2016 IEEE International Conference on Big Data, Big Data 2016 | Year: 2016

Expert finding is an important technique to obtain the user authority ranking in community question answering (CQA) websites. ZhihuRank is a topic-sensitive expert finding algorithm, which is based on both LDA and PageRank. Currently, with the amount of participants and documents increasing rapidly in CQA websites, how to parallel expert finding algorithms for big data analysis has received significant attention. In this paper, we find that the Spark framework is more suitable for paralleling expert finding algorithms than the MapReduce framework, which is a memory-based parallel computing model to support complicated iterative algorithms. As an example, we parallel ZhihuRank using MLlib's LDA and GraphX's PageRank in Spark. Experiments have been conducted on large-scale real data from Zhihu1 (the most popular CQA website in China). And the experimental results confirmed the effectiveness and scalability of our proposed approach. © 2016 IEEE.


Pu L.,Nankai University | Chen X.,Sun Yat Sen University | Chen X.,Key Laboratory of Machine Intelligence and Advanced Computing | Xu J.,Nankai University | Fu X.,University of Gottingen
IEEE Journal on Selected Areas in Communications | Year: 2017

Recent years have witnessed the proliferation of mobile crowdsourcing that brings a new opportunity to leverage human intelligence and movement behaviors to wider application areas. In parallel with the development of online centralized platforms, we look into the realization of self-organized mobile crowdsourcing drawing on opportunistic networks, and propose the Crowd Foraging framework, in which a mobile task requester can proactively recruit a massive crowd of opportunistic encountered mobile workers in real time for quick and high-quality results. We present a comprehensive framework model that fully integrates human behavior factors for modeling task profile, worker arrival, and work ability, and then introduce a service quality concept to indicate the expected service gain that a requester can enjoy when she recruits an arrival worker by jointly considering the work ability of workers as well as timeliness and reward of tasks. Furthermore, we formulate a sequential worker recruitment problem as an online multiple stopping problem to maximize the expected sum of service quality, and accordingly derive an optimal worker recruitment policy through the dynamic programming principle, which exhibits a nice threshold-based structure. We provide data-driven case studies to validate the assumptions used in the policy design, and conduct extensive trace-driven numerical evaluations, which demonstrate that our policy can achieve superior performance (e.g., improve more than 30% performance over classic policies). Besides, our Android prototype shows that the Crowd Foraging framework is cost-efficient, such as requiring less than 7 s and 6 J in terms of time and energy consumption for the optimal threshold calculation in our policy in most cases. © 1983-2012 IEEE.


Dong B.,Sun Yat Sen University | Wu W.,Guangdong Key Laboratory of Big Data Analysis and Processing | Yang Z.,Key Laboratory of Machine Intelligence and Advanced Computing | Li J.,Sun Yat Sen University
Proceedings - 12th International Conference on Mobile Ad-Hoc and Sensor Networks, MSN 2016 | Year: 2017

This paper comes up with a SDN based On-Demand Routing Protocol, SVAO, which separates data forwarding layer and network control layer, as in SDN, to enhance the data transmission efficiency within VANETs. The Roadside Service Unit plays the role of Local Controller and is in charge of selecting vehicles to forward packet within a road segment. All the vehicles state in the road. Correspondingly, a two-level design is used. Global Level is distributed and adopts a ranked query scheme to collect vehicle information and determine the road segments along which a message should be forwarded. And the Local Level is in charge of selecting forwarding vehicles in each road segment determined by the global level. We compare SVAO with popular ad-hoc network routing protocols, including OLSR, DSR, DSDV, and DB via simulations. We consider the impact of vehicle density, speed on data transmission rate and average packet delay. The simulation results show that in case of large network scales or high vehicle speed, SVAO performs better than the others. © 2016 IEEE.


Sun S.-T.,Sun Yat Sen University | Sun S.-T.,Key Laboratory of Machine Intelligence and Advanced Computing | Li X.-D.,Sun Yat Sen University | Li X.-D.,Key Laboratory of Machine Intelligence and Advanced Computing | And 2 more authors.
International Journal of Systems Science | Year: 2017

For nonlinear switched discrete-time systems with input constraints, this paper presents an open-closed-loop iterative learning control (ILC) approach, which includes a feedforward ILC part and a feedback control part. Under a given switching rule, the mathematical induction is used to prove the convergence of ILC tracking error in each subsystem. It is demonstrated that the convergence of ILC tracking error is dependent on the feedforward control gain, but the feedback control can speed up the convergence process of ILC by a suitable selection of feedback control gain. A switched freeway traffic system is used to illustrate the effectiveness of the proposed ILC law. © 2017 Informa UK Limited, trading as Taylor & Francis Group


Ji S.,Sun Yat Sen University | Ji S.,Southwest Jiaotong University | Lu L.,Hangzhou Normal University | Lu L.,University of Electronic Science and Technology of China | And 4 more authors.
New Journal of Physics | Year: 2017

Social networks constitute a new platform for information propagation, but its success is crucially dependent on the choice of spreaders who initiate the spreading of information. In this paper, we remove edges in a network at random and the network segments into isolated clusters. The most important nodes in each cluster then form a set of influential spreaders, such that news propagating from them would lead to extensive coverage and minimal redundancy. The method utilizes the similarities between the segmented networks before percolation and the coverage of information propagation in each social cluster to obtain a set of distributed and coordinated spreaders. Our tests of implementing the susceptible-infected-recovered model on Facebook and Enron email networks show that this method outperforms conventional centrality-based methods in terms of spreadability and coverage redundancy. The suggested way of identifying influential spreaders thus sheds light on a new paradigm of information propagation in social networks. © 2017 IOP Publishing Ltd and Deutsche Physikalische Gesellschaft.


Gong X.,Auburn University | Duan L.,Singapore University of Technology and Design | Chen X.,Sun Yat Sen University | Chen X.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhang J.,Arizona State University
IEEE Journal on Selected Areas in Communications | Year: 2017

The rapid growth of online social networks has strengthened wireless users' social relationships, which in turn has resulted in more data traffic due to network effect in the social domain. Nevertheless, the boosted demand for wireless services may challenge the limited wireless capacity. To build a thorough understanding, we study mobile users' data usage behavior by jointly considering the network effect due to their social relationships in the social domain and the congestion effect in the physical wireless domain. Specifically, we develop a Stackelberg game for socially aware data usage: in Stage I, a wireless provider first decides the data pricing to all users in order to maximize its revenue, and then in Stage II, users decide their data usage, for the given price, subject to mutual interactions under both social network effect and congestion effect. We analyze the two-stage game via backward induction. In particular, for Stage II, we first provide conditions for the existence and the uniqueness of a user demand equilibrium (UDE). Then, we propose algorithms to find the UDE and for users to reach the UDE in a distributed manner. We further investigate the impact of different system parameters on the UDE. Next, for Stage I, we develop an optimal pricing algorithm to maximize the wireless provider's revenue. We numerically evaluate the performance of our proposed algorithms using real data, and thereby draw useful engineering insights for the operation of wireless providers: 1) when social network effect dominates congestion effect, the marginal gain of the total usage increases with the social ties and the number of users, or decreases with the congestion coefficient; in contrast, when congestion effect dominates social network effect, the marginal gain decreases (or increases, respectively) with these parameters and 2) when social network effect is strong, a lower price should be set to increase the total revenue; in contrast, when congestion effect is strong, a higher price is preferred. © 1983-2012 IEEE.


Liu X.-F.,Sun Yat Sen University | Liu X.-F.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhan Z.-H.,Sun Yat Sen University | Zhan Z.-H.,Key Laboratory of Machine Intelligence and Advanced Computing | And 2 more authors.
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

Cloud computing provides resources as services in pay-as-yougo mode to customers by using virtualization technology. As virtual machine (VM) is hosted on physical server, great energy is consumed by maintaining the servers in data center. More physical servers means more energy consumption and more money cost. Therefore, the VM placement (VMP) problem is significant in cloud computing. This paper proposes an approach based on ant colony optimization (ACO) to solve the VMP problem, named as ACO-VMP, so as to effectively use the physical resources and to reduce the number of running physical servers. The number of physical servers is the same as the number of the VMs at the beginning. Then the ACO approach tries to reduce the physical server one by one. We evaluate the performance of the proposed ACO-VMP approach in solving VMP with the number of VMs being up to 600. Experimental results compared with the ones obtained by the first-fit decreasing (FFD) algorithm show that ACO-VMP can solve VMP more efficiently to reduce the number of physical servers significantly, especially when the number of VMs is large. © 2014 ACM.


Zhang W.-X.,Sun Yat Sen University | Chen W.-N.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhang J.,Key Laboratory of Machine Intelligence and Advanced Computing
Proceedings of the 8th International Conference on Advanced Computational Intelligence, ICACI 2016 | Year: 2016

In this paper, a dynamic competitive swarm optimizer (DCSO) based on population entropy is proposed. The new algorithm is derived from the competitive swarm optimizer (CSO). The new algorithm uses population entropy to make a quantitative description about the diversity of population, and to divide the population into two sub-groups dynamically. During the early stage of the execution process, to speed up convergence of the algorithm, the sub-group with better fitness will have a small size, and worse large sub-group will learn from small one. During the late stage of the execution process, to keep the diversity of the population, the sub-group with better fitness will have a large size, and small worse sub-group will learn from large group. The proposed DCSO is evaluated on CEC'08 benchmark functions on large scale global optimization. The simulation results of the example indicate that the new algorithm has better and faster convergence speed than CSO. © 2016 IEEE.


Li Y.-L.,Key Laboratory of Machine Intelligence and Advanced Computing | Li Y.-L.,Key Laboratory of Software Technology | Zhou Y.-R.,Key Laboratory of Machine Intelligence and Advanced Computing | Zhou Y.-R.,Key Laboratory of Software Technology | And 4 more authors.
IEEE Transactions on Evolutionary Computation | Year: 2016

Decomposition-based multiobjective evolutionary algorithms (MOEAs) have been studied a lot and have been widely and successfully used in practice. However, there are no related theoretical studies on this kind of MOEAs. In this paper, we theoretically analyze the MOEAs based on decomposition. First, we analyze the runtime complexity with two basic simple instances. In both cases the Pareto front have one-to-one map to the decomposed subproblems or not. Second, we analyze the runtime complexity on two difficult instances with bad neighborhood relations in fitness space or decision space. Our studies show that: 1) in certain cases, polynomial-sized evenly distributed weight parameters-based decomposition cannot map each point in a polynomial sized Pareto front to a subproblem; 2) an ideal serialized algorithm can be very efficient on some simple instances; 3) the standard MOEA based on decomposition can benefit a runtime cut of a constant fraction from its neighborhood coevolution scheme; and 4) the standard MOEA based on decomposition performs well on difficult instances because both the Pareto domination-based and the scalar subproblem-based search schemes are combined in a proper way. © 1997-2012 IEEE.

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