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Jung T.,Illinois Institute of Technology | Li X.-Y.,Illinois Institute of Technology | Li X.-Y.,Tsinghua University | Wan Z.,Tsinghua National Laboratory for Information Sciences and Technology | And 2 more authors.
IEEE Transactions on Information Forensics and Security | Year: 2016

Ma et al. recently submitted a comment correspondence which points out a flaw in our paper (a sequel of our earlier paper published in the Proceedings of IEEE INFOCOM). The flaw led to the leakage of the system-wide master key; therefore, we improved our own scheme by addressing it. © 2016 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission.

Zheng X.-L.,Hong Kong University of Science and Technology | Wan M.,Center for Science and Technology Development
Journal of Computer Science and Technology | Year: 2014

Wireless sensor networks (WSNs) have been applied in a variety of application areas. Most WSN systems, once deployed, are intended to operate unattended for a long period. During the lifetime, it is necessary to fix bugs, reconfigure system parameters, and upgrade the software in order to achieve reliable system performance. However, manually collecting all nodes back and reconfiguring through serial connections with computer is infeasible since it is labor-intensive and inconvenient due to the harsh deploying environments. Hence, data dissemination over multi-hop is desired to facilitate such tasks. This survey discusses the requirements and challenges of data dissemination in WSNs, reviews existing work, introduces some relevant techniques, presents the metrics of the performance and comparisons of the state-of-the-art work, and finally suggests the possible future directions in data dissemination studies. This survey elaborates and compares existing approaches of two categories: structure-less schemes and structure-based schemes, classified by whether or not the network structure information is used during the disseminating process. In existing literatures, different categories have definite boundary and limited analysis on the trade-off between different categories. Besides, there is no survey that discusses the emerging techniques such as Constructive Interference (CI) while these techniques have the chance to change the framework of data dissemination. In a word, even though many efforts have been made, data dissemination in WSNs still needs some more work to embrace the new techniques and improve the efficiency and practicability further. © 2014 Springer Science+Business Media New York & Science Press, China.

He L.,Tongji University | Hu D.,Fudan University | Wan M.,Center for Science and Technology Development | Wen Y.,East China Normal University | And 3 more authors.
IEEE Transactions on Systems, Man, and Cybernetics: Systems | Year: 2016

Modeling and learning of brain activity patterns represent a huge challenge to the brain-computer interface (BCI) based on electroencephalography (EEG). Many existing methods estimate the uncorrelated instantaneous demixing of EEG signals to classify multiclass motor imagery (MI). However, the condition of uncorrelation does not hold true in practice, because the brain regions work with partial or complete collaboration. This work proposes a novel method, termed as a common Bayesian network (CBN), to discriminate multiclass MI EEG signals. First, with the constraints of a Gaussian mixture model on every channel, only related channels are selected to construct a normal Bayesian network. Second, the nodes that have both common and varying edges are selected to construct a CBN. Third, the probabilities on common edges are used to learn about the support vector machine for classification. To validate the proposed method, we conduct experiments on two well-known BCI datasets and perform a numerical analysis of the propose algorithm for EEG classification in a multiclass MI BCI. Experimental results show that the proposed CBN method not only has excellent classification performance, but also is highly efficient. Hence, it is suitable for the cases where a system is required to respond within a second. © 2015 IEEE.

Zhang Chun-Lai C.-L.,Beijing Normal University | Yang S.,Center for Science and Technology Development | Pan X.-H.,Weifang Bureau of Hydrology and Water Resources Survey | Zhang J.-Q.,Beijing Normal University
Soil and Tillage Research | Year: 2011

Soil wind erosion in northern China has become a serious environmental problem, especially on cultivated soil, but data on soil loss by wind erosion are scarce due to limitation in effective measurement methods. The objective of this study is to introduce differential global positioning system (GPS) measurements into studies of wind erosion and obtain field measured data of soil wind erosion rates during previous decades in Kangbao County, a representative area suffering from serious farmland wind erosion in northern China. Vegetative banks that were maintained between farmland plots after the original grassland had been cultivated into stripped farmland were assumed to have experienced little erosion or deposition. As a result, these banks offer a useful reference for GPS measurements in estimations of soil loss by wind erosion in the adjacent farmland plots. Differential GPS and real-time kinematic (RTK) measurement show that almost a half centimetre of top soil has been blown away in this area every year since the soil had been cultivated in the early twentieth century. To examine the reliability of the GPS measurements, average wind erosion rate in this area was derived from the 137Cs model in 30 farmland plots, which was 0.55cma-1 or 89.5tha-1a-1. Results of both methods show the study area has been experiencing severe wind erosion, which has caused serious local environmental problems and made this area an important source of blown sand and dust that threatens the Beijing and the Tianjin metropolitan area. Both methods have advantages and limitations and generally each of them cannot be replaced by another; however, this paper presents an example that a combination of applying RTK GPS measurements in special plots with using 137Cs techniques on the regional scale produces very helpful results of regional wind erosion. © 2010 Elsevier B.V.

Wan M.,Center for Science and Technology Development | He L.,Tongji University
Jisuanji Yanjiu yu Fazhan/Computer Research and Development | Year: 2015

In this paper, we study two tightly coupled topics in selecting paper reviewers from authors' scientific collaboration network (SCN): network construction and community detection. Based on the fact that the authors of one journal can be selected as reviewers and the reviewers of one manuscript should come from different research communities, we firstly evaluate the collaboration among all authors according to their signatures and construct the normalized collaboration network. For the second key problem of detecting the communities of one scientific collaboration network, considering it is much sparse and has few connections with inter community for one vertex, we apply the method of orthogonal matching pursuit to calculate compressive collaboration information. We conduct several experiments on simulated and real journal author datasets. Although there is no standard to evaluate different kinds of scientific collaboration network, the community detection accuracy rate and the stability of all authors are used to evaluate the performance of the proposed method. We can see from the vertex linkage matrix that our designed scientific collaboration network has good character of vertex grouping. The extensive study of our detection method in simulated data shows that the proposed method has a great advantage in the detection rate and stability. The significant improvement is about 60% compared with the classic methods. ©, 2015, Science Press. All right reserved.

Jung T.,Illinois Institute of Technology | Li X.-Y.,Illinois Institute of Technology | Li X.-Y.,Tsinghua National Laboratory for Information Sciences and Technology | Wan Z.,Tsinghua National Laboratory for Information Sciences and Technology | And 2 more authors.
IEEE Transactions on Information Forensics and Security | Year: 2015

Cloud computing is a revolutionary computing paradigm, which enables flexible, on-demand, and low-cost usage of computing resources, but the data is outsourced to some cloud servers, and various privacy concerns emerge from it. Various schemes based on the attribute-based encryption have been proposed to secure the cloud storage. However, most work focuses on the data contents privacy and the access control, while less attention is paid to the privilege control and the identity privacy. In this paper, we present a semianonymous privilege control scheme AnonyControl to address not only the data privacy, but also the user identity privacy in existing access control schemes. AnonyControl decentralizes the central authority to limit the identity leakage and thus achieves semianonymity. Besides, it also generalizes the file access control to the privilege control, by which privileges of all operations on the cloud data can be managed in a fine-grained manner. Subsequently, we present the AnonyControl-F, which fully prevents the identity leakage and achieve the full anonymity. Our security analysis shows that both AnonyControl and AnonyControl-F are secure under the decisional bilinear Diffie-Hellman assumption, and our performance evaluation exhibits the feasibility of our schemes. © 2014 IEEE.

Wang W.,Shanghai JiaoTong University | Jiang X.,Shanghai JiaoTong University | Wang S.,Shanghai JiaoTong University | Wan M.,Center for Science and Technology Development | Sun T.,Shanghai JiaoTong University
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

With the extensive equipment of surveillance systems, the assessment of the integrity of surveillance videos is of vital importance. In this paper, an algorithm based on optical flow and anomaly detection is proposed to authenticate digital videos and further identify the inter-frame forgery process (i.e. frame deletion, insertion, and duplication). This method relies on the fact that forgery operation will introduce discontinuity points to the optical flow variation sequence and these points show different characteristics depending on the type of forgery. The anomaly detection scheme is adopted to distinguish the discontinuity points. Experiments were performed on several real-world surveillance videos delicately forged by volunteers. The results show that the proposed algorithm is effective to identify forgery process with localization, and is robust to some degree of MPEG compression. © 2014 Springer-Verlag Berlin Heidelberg.

Jung T.,Illinois Institute of Technology | Li X.-Y.,Illinois Institute of Technology | Wan M.,Center for Science and Technology Development
IEEE Transactions on Dependable and Secure Computing | Year: 2015

Much research has been conducted to securely outsource multiple parties' data aggregation to an untrusted aggregator without disclosing each individual's privately owned data, or to enable multiple parties to jointly aggregate their data while preserving privacy. However, those works either require secure pair-wise communication channels or suffer from high complexity. In this paper, we consider how an external aggregator or multiple parties can learn some algebraic statistics (e.g., sum, product) over participants' privately owned data while preserving the data privacy. We assume all channels are subject to eavesdropping attacks, and all the communications throughout the aggregation are open to others. We first propose several protocols that successfully guarantee data privacy under semi-honest model, and then present advanced protocols which tolerate up to $k$ passive adversaries who do not try to tamper the computation. Under this weak assumption, we limit both the communication and computation complexity of each participant to a small constant. At the end, we present applications which solve several interesting problems via our protocols. © 2004-2012 IEEE.

Lin Y.,Sun Yat Sen University | Li J.-J.,South China Normal University | Zhang J.,Sun Yat Sen University | Wan M.,Center for Science and Technology Development
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

Evolution mechanisms of different biological and social systems have inspired a variety of evolutionary computation (EC) algorithms. However, most existing EC algorithms simulate the evolution procedure at the individual-level. This paper proposes a new EC mechanism inspired by the evolution procedure at the tribe-level, namely tribal ecosystem inspired algorithm (TEA). In TEA, the basic evolution unit is not an individual that represents a solution point, but a tribe that covers a subarea in the search space. More specifically, a tribe represents the solution set locating in a particular subarea with a coding structure composed of three elements: tribal chief, attribute diversity, and advancing history. The tribal chief represents the locally best-so-far solution, the attribute diversity measures the range of the subarea, and the advancing history records the local search experience. This way, the new evolution unit provides extra knowledge about neighborhood profiles and search history. Using this knowledge, TEA introduces four evolution operators, reforms, self-advance, synergistic combination, and augmentation, to simulate the evolution mechanisms in a tribal ecosystem, which evolves the tribes from potentially promising subareas to the global optimum. The proposed TEA is validated on benchmark functions. Comparisons with three representative EC algorithms confirm its promising performance. © 2014 ACM.

Yu W.-J.,Sun Yat Sen University | Yu W.-J.,Key Laboratory of Machine Intelligence and Advanced Computing | Li J.-J.,South China Normal University | Zhang J.,Sun Yat Sen University | Wan M.,Center for Science and Technology Development
GECCO 2014 - Proceedings of the 2014 Genetic and Evolutionary Computation Conference | Year: 2014

Differential evolution (DE) has been demonstrated to be one of the most promising evolutionary algorithms (EAs) for global numerical optimization. DE mainly differs from other EAs in that it employs difference of the parameter vectors in mutation operator to search the objective function landscape. Therefore, the performance of a DE algorithm largely depends on the design of its mutation strategy. In this paper, we propose a new kind of DE mutation strategies whose greediness degree can be adaptively adjusted. The proposed mutation strategies utilize the information of top t solutions in the current population. Such a greedy strategy is beneficial to fast convergence performance. In order to adapt the degree of greediness to fit for different optimization scenarios, the parameter t is adjusted in each generation of the algorithm by an adaptive control scheme. This way, the convergence performance and the robustness of the algorithm can be enhanced at the same time. To evaluate the effectiveness of the proposed adaptive greedy mutation strategies, the approach is applied to original DE algorithms, as well as DE algorithms with parameter adaptation. Experimental results indicate that the proposed adaptive greedy mutation strategies yield significant performance improvement for most of cases studied. © 2014 ACM.

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